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Showing papers in "Iet Image Processing in 2016"


Journal ArticleDOI
TL;DR: Automatic ABCD scoring of dermoscopy lesions is implemented and the experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.
Abstract: The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.

187 citations


Journal ArticleDOI
TL;DR: This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and human visual system in the discrete wavelet transform (DWT) domain that outperformed several previous schemes in terms of imperceptibility and robustness.
Abstract: Digital watermarking has been suggested as a way to achieve digital protection. The aim of digital watermarking is to insert the secret data into the image without significantly affecting the visual quality. This study presents a robust block-based image watermarking scheme based on the singular value decomposition (SVD) and human visual system in the discrete wavelet transform (DWT) domain. The proposed method is considered to be a block-based scheme that utilises the entropy and edge entropy as HVS characteristics for the selection of significant blocks to embed the watermark, which is a binary watermark logo. The blocks of the lowest entropy values and edge entropy values are selected as the best regions to insert the watermark. After the first level of DWT decomposition, the SVD is performed on the low-low sub-band to modify several elements in its U matrix according to predefined conditions. The experimental results of the proposed scheme showed high imperceptibility and high robustness against all image processing attacks and several geometrical attacks using examples of standard and real images. Furthermore, the proposed scheme outperformed several previous schemes in terms of imperceptibility and robustness. The security issue is improved by encrypting a portion of the important information using Advanced Standard Encryption a key size of 192-bits (AES-192).

160 citations


Journal ArticleDOI
TL;DR: The proposed system was tested on well-known images like Lena, NebulaM83, Mandrill and Clown and succeeded to reduce the encryption time by saving the mixed chaotic maps and reusing them as need arose.
Abstract: The need for image encryption is constantly on the rise and has led to the emergence of many techniques in literature. In recent years, many chaos-based encryption techniques have developed with more or less success. In this study, we suggest one such approach using two oscillators. Mixed chaotic maps from the Colpitts and Duffing oscillators were used to encrypt images, which helped to increase the key space to 2448 ≃ 7.26 × 10134. The authors also succeeded to reduce the encryption time by saving the mixed chaotic maps and reusing them as need arose. The proposed system was tested on well-known images like Lena, NebulaM83, Mandrill and Clown.

68 citations


Journal ArticleDOI
TL;DR: Two image filtering methods, playing the roles of denoising and maintaining detail information are utilised in the new algorithm, and the parameters for balancing these two parts are computed by measuring the variance of grey-level values in each neighbourhood.
Abstract: Adding spatial penalty terms in fuzzy c-means (FCM) models is an important approach for reducing the noise effects in the process of image segmentation. Though these algorithms have improved the robustness to noises in a certain extent, they still have some shortcomings. First, they are usually very sensitive to the parameters which are supposed to be tuned according to noise intensities. Second, in the case of inhomogeneous noises, using a constant parameter for different image regions is obviously unreasonable and usually leads to an unideal segmentation result. For overcoming these drawbacks, a noise detecting-based adaptive FCM for image segmentation is proposed in this study. Two image filtering methods, playing the roles of denoising and maintaining detail information are utilised in the new algorithm. The parameters for balancing these two parts are computed by measuring the variance of grey-level values in each neighbourhood. Numerical experiments on both synthetic and real-world image data show that the new algorithm is effective and efficient.

67 citations


Journal ArticleDOI
TL;DR: The quantitative and visual assessment shows that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms and results in natural-looking, good contrast images with almost no artefacts.
Abstract: This study presents a new contrast-enhancement approach called entropy-based dynamic sub-histogram equalisation. The proposed algorithm performs a recursive division of the histogram based on the entropy of the sub-histograms. Each sub-histogram is divided recursively into two sub-histograms with equal entropy. A stopping criterion is proposed to achieve an optimum number of sub-histograms. A new dynamic range is allocated to each sub-histogram based on the entropy and number of used and missing intensity levels in the sub-histogram. The final contrast-enhanced image is obtained by equalising each sub-histogram independently. The proposed algorithm is compared with conventional as well as state-of-the-art contrast-enhancement algorithms. The quantitative results for a large image data set are statistically analysed using a paired t-test. The quantitative and visual assessment shows that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed algorithm results in natural-looking, good contrast images with almost no artefacts.

60 citations


Journal ArticleDOI
TL;DR: This study presents a novel chaotic–neural network of image encryption and decryption image applied to the domain of medical to ensure the safety of medical images with a less complex algorithm compared with the existing methods.
Abstract: This study presents a novel chaotic–neural network of image encryption and decryption image applied to the domain of medical. The main objective behind the proposed technique is to ensure the safety of medical images with a less complex algorithm compared with the existing methods. In order to improve the robustness, the totality of the pixels related to the host image is XORed with a generation key. After that, with a chaotic system (logistic map), the binary sequence is generated in order to set the weights wij and bias bi of neuron network with the goal of encrypting the pixels issued from the previous step. Simulation and experiments were carried out on medical images coded on 8 and 12 bits/pixel. The obtained results confirmed the performance and the efficiency of the proposed method, which is compliant with Digital Imaging and Communications in Medicine standards.

53 citations


Journal ArticleDOI
TL;DR: A new, turtle shell-based data hiding scheme is proposed to improve embedding capacity further while guaranteeing good image quality and the experimental results indicated that the proposed scheme achieved higher embeddingcapacity and lower distortion of images than some existing schemes.
Abstract: Data hiding is a technique for sending secret information under the cover of the digital media. It is usually used to protect privacy and sensitive information when such information is transmitted via a public network. To date, high capacity remains one of the most important research aspects of data hiding. In this study, a new, turtle shell-based data hiding scheme is proposed to improve embedding capacity further while guaranteeing good image quality. In the proposed, turtle shell-based scheme, a reference matrix is composed and a location table is generated. Then, according to the reference matrix and the location table, each pixel pair is processed to conceal four secret bits. The experimental results indicated that the proposed scheme achieved higher embedding capacity and lower distortion of images than some existing schemes.

53 citations


Journal ArticleDOI
TL;DR: In this paper, a color-texture image segmentation using neutrosophic set (NS) and non-subsampled contourlet transform (NSCT) is proposed.
Abstract: The process of partitioning an image into some different meaningful regions with the homogeneous characteristics is called the image segmentation which is a crucial task in image analysis. This study presents an efficient scheme for unsupervised colour–texture image segmentation using neutrosophic set (NS) and non-subsampled contourlet transform (NSCT). First, the image colour and texture information are extracted via CIE Luv colour space model and NSCT, respectively. Then, the extracted colour and texture information are transformed into the NS domain efficiently by the authors’ proposed approach. In the NS-based image segmentation, the indeterminacy assessment of the images in the NS domain is notified by the entropy concept. The lower quantity of indeterminacy in the NS domain, the higher confidence and easier segmentation could be achieved. Therefore, to achieve a better segmentation result, an appropriate indeterminacy reduction operation is proposed. Finally, the K-means clustering algorithm is applied to perform the image segmentation in which the cluster number K is determined by the cluster validity analysis. To show the effectiveness of their proposed method, its performance is compared with that of the state-of-the-art methods. The experimental results reveal that their segmentation scheme outperforms the other methods for the Berkeley dataset.

51 citations


Journal ArticleDOI
TL;DR: Experimental results show that the proposed colour image water marking technique based on Hessenberg decomposition outperforms other watermarking methods and it is robust to resist a wide range of attacks, e.g. image compression, filtering, cropping, rotation, adding noise, blurring, scaling, sharpening and rotation and so on.
Abstract: In this study, a novel blind image watermarking technique using Hessenberg decomposition is proposed to embed colour watermark image into colour host image. In the process of embedding watermark, the watermark information of colour image is embedded into the second row of the second column element and the third row of the second column element in the orthogonal matrix obtained by Hessenberg decomposition. In the process of extracting watermark, neither the original host image nor the original watermark image is needed and it is impossible to retrieve them without the authorised keys. Experimental results show that the proposed colour image watermarking technique based on Hessenberg decomposition outperforms other watermarking methods and it is robust to resist a wide range of attacks, e.g. image compression, filtering, cropping, rotation, adding noise, blurring, scaling, sharpening and rotation and so on. Especially, the proposed method has lower computational complexity than other methods based on singular value decomposition or QR decomposition.

50 citations


Journal ArticleDOI
TL;DR: This study proposes a novel approach for appearance-based facial feature extraction to perform the task of facial expression recognition on video sequences and shows superior performance compared with the state-of-the-art approaches.
Abstract: A key issue regarding feature extraction is the capability of a technique to extract distinctive features to represent facial expressions while requiring a low computational complexity. In this study, the authors propose a novel approach for appearance-based facial feature extraction to perform the task of facial expression recognition on video sequences. The proposed spatiotemporal texture map (STTM) is capable of capturing subtle spatial and temporal variations of facial expressions with low computational complexity. First, face is detected using Viola–Jones face detector and frames are cropped to remove unnecessary background. Facial features are then modelled with the proposed STTM, which uses the spatiotemporal information extracted from three-dimensional Harris corner function. A block-based method is adopted to extract the dynamic features and represent the features in the form of histograms. The features are then classified into classes of emotion by the support vector machine classifier. The experimental results demonstrate that the proposed approach shows superior performance compared with the state-of-the-art approaches with an average recognition rate of 95.37, 98.56, and 84.52% on datasets containing posed expressions, spontaneous micro-expressions, and close-to-real-world expressions, respectively. They also show that the proposed algorithm requires low computational cost.

48 citations


Journal ArticleDOI
TL;DR: A novel image enhancement approach based on intuitionistic fuzzy sets is proposed, which first divides an image into sub-object and sub-background areas, and then successively implements new fuzzification, hyperbolisation, and defuzzification operations on each area.
Abstract: Enhancement of images with weak edges faces great challenges in imaging applications. In this study, the authors propose a novel image enhancement approach based on intuitionistic fuzzy sets. The proposed method first divides an image into sub-object and sub-background areas, and then successively implements new fuzzification, hyperbolisation, and defuzzification operations on each area. In this way, an enhanced image is obtained, where the visual quality of region of interest (ROI) is significantly improved. Several types of images are utilised to validate the proposed method with respect to the enhancement performance. Experimental results demonstrate that the proposed algorithm not only works more stably for different types of images, but also has better enhancement performance, in comparison to conventional methods. This is a great merit of such design for discerning specific ROIs.

Journal ArticleDOI
TL;DR: This work proposes a novel image watermarking method using LCWT and QR decomposition that is not only feasible, but also robust to some geometry attacks and image processing attacks.
Abstract: Inspired by the fact that wavelet transform can be written as a classical convolution form, a new linear canonical wavelet transform (LCWT) based on generalised convolution theorem associated with linear canonical transform (LCT) is proposed recently. The LCWT not only inherits the advantages of multi-resolution analysis of wavelet transform (WT), but also has the capability of image representations in the LCT domain. Based on these good properties, the authors propose a novel image watermarking method using LCWT and QR decomposition. Compared with the existing image watermarking methods based on discrete WT or QR, this novel image watermarking method provides more flexibility in the image watermarking. Peak signal-to-noise ratio, normalised cross and structural similarity index measure are used to verify the advantages of the proposed method in simulation experiments. The experiment results show that the proposed method is not only feasible, but also robust to some geometry attacks and image processing attacks.

Journal ArticleDOI
TL;DR: Experimental results indicate that the proposed algorithm can simultaneously enhance the low-light images and reduce noise effectively and could also perform quite well compared with the current common image enhancement and noise reduction algorithms in terms of the subjective visual effects and objective quality assessments.
Abstract: Images obtained under low-light conditions tend to have the characteristics of low-grey levels, high-noise levels, and indistinguishable details. Image degradation not only affects the recognition of images, but also influences the performance of the computer vision system. The low-light image enhancement algorithm based on the dark channel prior de-hazing technique can enhance the contrast of images effectively and can highlight the details of images. However, the dark channel prior de-hazing technique ignores the effects of noise, which leads to significant noise amplification after the enhancement process. In this study, a de-hazing-based simultaneous enhancement and noise reduction algorithm of are proposed by analysing the essence of the dark channel prior de-hazing technique and bilateral filter. First, the authors estimate the values of the initial parameters of the hazy image model by de-hazing technique. Then, they correct the parameters of the hazy image model alternately with the iterative joint bilateral filter. Experimental results indicate that the proposed algorithm can simultaneously enhance the low-light images and reduce noise effectively. The proposed algorithm could also perform quite well compared with the current common image enhancement and noise reduction algorithms in terms of the subjective visual effects and objective quality assessments.

Journal ArticleDOI
TL;DR: A novel method is presented to improve the visibility of a single input hazy image by proposing a multi-scale gradient domain contrast enhancement approach that handles the different residual images rather than the entire image, and correct the attenuation of colour according to the estimated transmission.
Abstract: Outdoor images captured under bad weathers often suffer from low visibility. In this study, a novel method is presented to improve the visibility of a single input hazy image. On the basis of the observation that degradation of a hazy image occurs both in contrast and colour, the authors method aims at compensating the contrast and colour of the image, respectively. To achieve this, they propose a multi-scale gradient domain contrast enhancement approach that handles the different residual images rather than the entire image, and correct the attenuation of colour according to the estimated transmission. Since there is no need to recover the scene radiance by the degradation model, their method depends less on the accuracy of transmission and does not require the estimation of atmospheric light. Experiments on a variety types of hazy images show that their method yields accurate results with fine details and vivid colour, even better than other state-of-the-art dehazing methods.

Journal ArticleDOI
TL;DR: A variational method based on Gamma distribution in the neutrosophic domain to improve clinical diagnosis and to enhance quality of ultrasound images by suppresses the speckle noise as well as preserves the textures and fine details is presented.
Abstract: Neutrosophy is a useful tool for handling uncertainty associated with the images and widely used in image denoising. Speckle noise is inherent in ultrasound images, which generally tends to reduce resolution and contrast, thereby degrading the diagnostic accuracy. This paper presents a variational method based on Gamma distribution in the neutrosophic domain to improve clinical diagnosis and to enhance quality of ultrasound images. In this method, image is transformed into the neutrosophic (NS) domain via three membership subsets (true, indeterminate and false). Then, the filtering operation is applied based on total variation regularisation to reduce the indeterminacy of the image, which is measured by the entropy of an indeterminate set. The proposed speckle reduction method has been assessed on both the artificial speckle simulated images and real US images. The experimental results reveal the superiority of the proposed method in terms of both quantitatively and qualitatively as compared to other speckle reduction methods reported in the literature. Furthermore, the visual evaluation of despeckled images demonstrates that the proposed method suppresses the speckle noise as well as preserves the textures and fine details.

Journal ArticleDOI
TL;DR: In this study, the embedding strength parameters for per-block image watermarking in the discrete cosine transform (DCT) domain are optimised and the Bees algorithm is selected as the optimisation method and the proposed fitness function is applied.
Abstract: The design of a robust watermarking technique has been always suffering from the conflict between the watermark robustness and the quality of the watermarked image. In this study, the embedding strength parameters for per-block image watermarking in the discrete cosine transform (DCT) domain are optimised. A fitness function is proposed to best suit the optimisation problem. The optimum solution is selected based on the quality and the robustness achieved using that solution. For a given image block, the peak-signal-to-noise ratio (PSNR) is used as a quality metric to measure the imperceptibility for the watermarked block. However, the robustness cannot be measured for a single watermark bit using traditional metrics. The proposed method uses the PSNR quality metric to indicate the degree of robustness. Hence, optimum embedding in terms of quality and robustness can be achieved. To demonstrate the effectiveness of the proposed approach, a recent watermarking technique is modified, and then used as the embedding method to be optimised. The Bees algorithm is selected as the optimisation method and the proposed fitness function is applied. Experimental results show that the proposed method provides enhanced imperceptibility and robustness under different attacks.

Journal ArticleDOI
TL;DR: A fast CU decision method is proposed, which contains two steps: first, the depth to begin searching is determined according to the deviation of the LCU and then splitting the current CU further is decided according to RDcost, and a fast mode selection method to reduce complexity.
Abstract: High-efficiency video coding (HEVC) is a new video coding compression standard. As the successor to H.264/AVC, it provides better performance and supports higher resolution. However, the encoding complexity increases drastically. One of the major reasons is that the coding unit (CU) in HEVC is multi-sized and adjustable rather than fixed as in H.264. In addition, the number of prediction modes used in intra-frame coding is expanded from 9 to 35. The authors analysed the statistical correlations of CU depth to the deviation of pixels in the largest coding unit (LCU) and rate-distortion cost (RDcost). Accordingly, a fast CU decision method is proposed, which contains two steps: first, the depth to begin searching is determined according to the deviation of the LCU and then splitting the current CU further is decided according to RDcost. For intra-prediction, we also propose a fast mode selection method to reduce complexity. This method can quickly determine the modes for rate-distortion optimisation when the combination of most probable modes reveals the pattern direction. Software simulations show that the proposed methods reduce encoding time by more than 50% with an average of 1.4% increase of BD-rate compared to reference software HM12.0.

Journal ArticleDOI
TL;DR: A method where appropriate NNs are used only at the image decompression stage to produce an acceptable and comparable image quality.
Abstract: Neural networks (NNs) have been used for image compression for their good performance. However, the image compression convergence time is not efficient. This is due to the fact that the NN is used in image compression and decompression stages in almost all NN methods. The authors propose a method where appropriate NNs are used only at the image decompression stage. The image is decomposed into eight matrices each of which corresponds to values in a bit position. The matrices are saved in reduced form to constitute the compressed image. The NNs are constructed to predict the removed values from the reduced matrices to produce the image in the origin size. This method produces an acceptable and comparable image quality. A compression ratio of up to 81% was achieved while the convergence time can be considered negligible.

Journal ArticleDOI
TL;DR: A real-time vehicle detection algorithm which is based on the improved Haar-like features and combines motion detection with a cascade of classifiers and has been successfully evaluated on the public datasets, which demonstrates its robustness and real- time performance.
Abstract: The strategy based on Haar-like features and the cascade classifier for vehicle detection systems has captured growing attention for its effectiveness and robustness; however, such a vehicle detection strategy relies on exhaustive scanning of an entire image with different sizes sliding windows, which is tedious and inefficient, since a vehicle only occupies a small part of the whole scene. Therefore, the authors propose a real-time vehicle detection algorithm which is based on the improved Haar-like features and combines motion detection with a cascade of classifiers. They adopt a visual background extractor, accompanied by morphological processing, to obtain foregrounds. These foregrounds retain vehicle features and provide the positions within images where vehicles are most likely to be located. Subsequently, vehicle detection is performed only at these positions by using a cascade of classifiers instead of a single strong classifier, which is able to improve the detection performance. The authors' algorithm has been successfully evaluated on the public datasets, which demonstrates its robustness and real-time performance.

Journal ArticleDOI
TL;DR: The proposed approach escapes the complicated treatment to highlight areas in the process of expansion, which makes the expansion straightforward; at the same time, it facilitates the expansion scheme and minimises the formation of the artefacts.
Abstract: Due to the growing popularity of high-dynamic range (HDR) image and the high complexity to capture HDR image, researchers focus on converting low-dynamic range (LDR) content to HDR, which gives rise to a number of dynamic range expansion methods. Most of the existing methods try their best to tackle highlight areas during the expanding, however, in some cases, they cannot achieve approving results. In this study, a novel LDR image expansion technique is presented. The technique first detects the highlight areas in image; then preprocesses them and reconstructs the information of these regions; finally, expands the LDR image to HDR. Unlike the existing schemes, the proposed approach escapes the complicated treatment to highlight areas in the process of expansion, which makes the expansion straightforward; at the same time, it facilitates the expansion scheme and minimises the formation of the artefacts. The experimental results show that the proposed method performs well; the tone mapped versions of the produced HDR images are popular. The results of the image quality metric also illustrate that the novel approach can recover more image details with minimised contrast loss and reversal, compared with the existing schemes considered in the comparison.

Journal ArticleDOI
TL;DR: An efficient scene-adaptive single image dehazing approach via opening dark channel model (ODCM) to optimise the whole atmospheric veil, in which the values of close view are regularised by a minimum channel image while the distant parts are estimated by an appropriate lower constant.
Abstract: Many traditional dark channel prior based haze removal schemes often suffer from the colour distortion and generate halo artefacts in the remote scenes. To tackle these issues, the authors present an efficient scene-adaptive single image dehazing approach via opening dark channel model (ODCM). First, the authors detect the image depth information and separate it into close view and distant view. Then, an ODCM is proposed to optimise the whole atmospheric veil, in which the values of close view are regularised by a minimum channel image while the distant parts are estimated by an appropriate lower constant. Accordingly, the transmission map can be further optimised by guide filter and smoothed by domain transform filter. Finally, the haze degraded image can be well restored by the atmosphere scattering model. The extensive experiments have shown that the proposed image dehazing approach has significantly increased the perceptual visibility of the scene and achieved a better colour fidelity visually.

Journal ArticleDOI
TL;DR: An unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos by applying a group sparse topical coding framework and an improved version of it to optical flow features extracted from video clips.
Abstract: In visual surveillance, detecting and localising abnormal events are of great interest. In this study, an unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos. For learning typical motion patterns occurring in such videos, a group sparse topical coding (GSTC) framework and an improved version of it are applied to optical flow features extracted from video clips. Then a very simple and efficient algorithm is proposed for GSTC. It is shown that discovered motion patterns can be employed directly in detecting abnormal events. A variety of abnormality metrics based on the resulting sparse codes for detection of abnormality are investigated. Experiments show that the result of the approach in detection and localisation of abnormal events is promising. In comparison with other usual methods (probabilistic latent semantic analysis, latent Dirichlet allocation, sparse topical coding (STC) and improved STC), according to the values of area under ROC, the proposed method achieves at least 14% improvement in abnormal event detection.

Journal ArticleDOI
TL;DR: The efficiency and robustness of the proposed method improves the target signal to the background noise and clutter and increases the contrast between them and also detects the small target in IR images with low false alarm rates and high reliability.
Abstract: Detection of dim small targets in infrared (IR) images with high reliability is very important in defence systems. In this study, a new method is introduced based on human visual system and saliency maps fusion to detect the small target in IR images with high reliability. By using the static and motion saliency maps fusion, emphasizing the obtained saliencies from one method to another and applying the information and benefits of all maps in the saliency map fusion, this method suppresses the background clutter and noise with high reliability, makes the target more prominent and finally increases the contrast among them. The experiments are carried out on some real-life data of IR images containing the moving target. The obtained results show the efficiency and robustness of the proposed method so that this method improves the target signal to the background noise and clutter and increases the contrast between them and also detects the small target in IR images with low false alarm rates and high reliability.

Journal ArticleDOI
TL;DR: A novel similarity measure has been proposed by integrating the effectiveness of each voxel along with the intensity distributions for computing the enhanced MI using joint histogram of the two images to achieve better registration accuracy as compared with existing methods with efficient computational runtime.
Abstract: Similarity measure plays a significant task in intensity-based image registration. Nowadays, mutual information (MI) has been used as an efficient similarity measure for multimodal image registration. MI reflects the quantitative aspects of the information as it considers the probabilities of the voxels. However, different voxels have distinct efficiency towards the gratification of the elementary target, which may be self-reliant of their probability of occurrence. Therefore, both intensity distributions and effectiveness are essential to characterise a voxel. In this study, a novel similarity measure has been proposed by integrating the effectiveness of each voxel along with the intensity distributions for computing the enhanced MI using joint histogram of the two images. Penalised spline interpolation is incorporated to the joint histogram of the similarity measure, where each grid point is penalised with a weighted factor to avoid the local extrema and to achieve better registration accuracy as compared with existing methods with efficient computational runtime. To demonstrate the proposed method, the authors have used a challenging medical image dataset consisting of pre- and post-operative brain magnetic resonance imaging. The registration accuracy for the dataset improves the clinical diagnosis, and detection of growth of tumour in post-operative image.

Journal ArticleDOI
TL;DR: This study surveys methods that are mainly designed for enhancing low-resolution textual images in super-resolution (SR) task and criticises these methods and discusses areas which promise improvements in such task.
Abstract: Super-resolution (SR) task has become an important research area due to the rapidly growing interest for high quality images in various computer vision and pattern recognition applications. This has led to the emergence of various SR approaches. According to the number of input images, two kinds of approaches could be distinguished: single or multi-input based approaches. Certainly, processing multiple inputs could lead to an interesting output, but this is not the case mainly for textual image processing. This study focuses on single image-based approaches. Most of the existing methods have been successfully applied on natural images. Nevertheless, their direct application on textual images is not enough efficient due to the specificities that distinguish these particular images from natural images. Therefore, SR approaches especially suited for textual images are proposed in the literature. Previous overviews of SR methods have been concentrated on natural images application with no real application on the textual ones. Thus, this study aims to tackle this lack by surveying methods that are mainly designed for enhancing low-resolution textual images. The authors further criticise these methods and discuss areas which promise improvements in such task. To the best of the authors’ knowledge, this survey is the first investigation in the literature.

Journal ArticleDOI
TL;DR: Experimental results show that the proposed novel haze-removal method can restore inland waterway images effectively; restored images are more natural and smoother than those obtained by the state-of-the-art haze removal algorithms.
Abstract: Haze significantly degrades the visibility for ship navigation and traffic monitoring in China's inland waterways. In this study, the authors propose a novel haze-removal method based on sky segmentation and dark channel prior to restore images. Sky segmentation is accomplished by using robust image matting and region growth algorithms. Then, the average image intensity of the sky region is chosen as the atmospheric light value to address the defect of dark channel prior. Experimental results show that their method can restore inland waterway images effectively; restored images are more natural and smoother than those obtained by the state-of-the-art haze removal algorithms.

Journal ArticleDOI
TL;DR: A new image denoising method for impulse noise in greyscale images using a context-based prediction scheme is presented, which preserves the details in the filtered images better than other methods.
Abstract: A new image denoising method for impulse noise in greyscale images using a context-based prediction scheme is presented. The algorithm replaces the noisy pixel with the value occurring with the highest frequency, in the same context as the replaceable pixel. Since it is a context-based technique, it preserves the details in the filtered images better than other methods. In the aim of validation, the authors have compared the proposed method with several existing denoising methods, many of them being outperformed by the proposed filter.

Journal ArticleDOI
TL;DR: Firefly algorithm (FA) is utilised to enhance Otsu's method in the direction of finding optimal multilevel thresholds using the maximum variance intra-clusters and will validate the effectiveness of the proposed solution to multi-label image annotation and label ranking problem.
Abstract: As digital images are increasing exponentially; it is very attractive to develop more effective machine learning frameworks for automatic image annotation. In order to address the most prominent issues (huge inter-concept visual similarity and huge intra-concept visual diversity) more effectively, an inter-related non-parametric Bayesian classifier training framework to support multi-label image annotation is developed. For this purpose, an image is viewed as a bag, and its instances are the over-segmented regions within it found automatically with an adopted Otsu's method segmentation algorithm. Here firefly algorithm (FA) is utilised to enhance Otsu's method in the direction of finding optimal multilevel thresholds using the maximum variance intra-clusters. FA has high convergence speed and less computation rate as compared with some evolutionary algorithms. By generating blobs, the extracted features for segmented regions, the concepts which are learned by the classifier tend to relate textually to the words which occur most often in the data and visually to the easiest to recognise segments. This allowing the opportunity to assign a word to each object (localised labelling). Extensive experiments on Corel benchmark image datasets will validate the effectiveness of the proposed solution to multi-label image annotation and label ranking problem.

Journal ArticleDOI
TL;DR: An automatic chessboard corner detection algorithm is presented for camera calibration that only requires a user input of the chessboard size, while all the other parameters can be adaptively calculated with a statistical approach.
Abstract: Chessboard corner detection is a necessary procedure of the popular chessboard pattern-based camera calibration technique, in which the inner corners on a two-dimensional chessboard are employed as calibration markers. In this study, an automatic chessboard corner detection algorithm is presented for camera calibration. In authors' method, an initial corner set is first obtained with an improved Hessian corner detector. Then, a novel strategy that utilises both intensity and geometry characteristics of the chessboard pattern is presented to eliminate fake corners from the initial corner set. After that, a simple yet effective approach is adopted to sort the detected corners into a meaningful order. Finally, the sub-pixel location of each corner is calculated. The proposed algorithm only requires a user input of the chessboard size, while all the other parameters can be adaptively calculated with a statistical approach. The experimental results demonstrate that the proposed method has advantages over the popular OpenCV chessboard corner detection method in terms of detection accuracy and computational efficiency. Furthermore, the effectiveness of the proposed method used for camera calibration is also verified in authors' experiments.

Journal ArticleDOI
TL;DR: A fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance.
Abstract: High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed-up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed-up in terms of encoding time and bit rate saving with tolerable peak signal-to-noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.