Showing papers on "Peak signal-to-noise ratio published in 2016"

Learning joint demosaicing and denoising based on sequential energy minimization

Abstract: Demosaicing is an important first step for color image acquisition. For practical reasons, demosaicing algorithms have to be both efficient and yield high quality results in the presence of noise. The demosaicing problem poses several challenges, e.g. zippering and false color artifacts as well as edge blur. In this work, we introduce a novel learning based method that can overcome these challenges. We formulate demosaicing as an image restoration problem and propose to learn efficient regularization inspired by a variational energy minimization framework that can be trained for different sensor layouts. Our algorithm performs joint demosaicing and denoising in close relation to the real physical mosaicing process on a camera sensor. This is achieved by learning a sequence of energy minimization problems composed of a set of RGB filters and corresponding activation functions. We evaluate our algorithm on the Microsoft Demosaicing data set in terms of peak signal to noise ratio (PSNR) and structured similarity index (SSIM). Our algorithm is highly efficient both in image quality and run time. We achieve an improvement of up to 2.6 dB over recent state-of-the-art algorithms.

A Watermarking Technique with Chaotic Fractional S-Box Transformation

Abstract: In this paper, the system of non-linear ordinary differential equations which defines a continuous-time dynamical system that shows the fractal characteristics of attractor is used to construct chaotic S-box. In this new digital watermarking technique the priority is of importance of robustness along with chaos to create confusion. The inclusion of chaos along with watermarking in frequency domain ensures robustness. As we have proposed a frequency domain watermarking as which we embed watermark into the low or middle frequencies, these changes will be spread all over the image. The strength of fractional S-box is evaluated with the help of bit independence criterion, nonlinearity analysis, strict avalanche criterion, linear approximation probability and differential approximation probability. Additionally, some security analyses in the form of correlation, contrast, energy, entropy, homogeneity, mean square error and peak signal to noise ratio are performed for validity of proposed watermarking scheme. The confidence measure after these analyses indicates the survival aligned with malicious attacks like noise, cropping and compression.

RGB-Based Secure Share Creation in Visual Cryptography Using Optimal Elliptic Curve Cryptography Technique

Abstract: Many shares are generated from the secret images that are illogical containing certain message within them in visual cryptography. When all shares are piled jointly, they tend to expose the secret of the image. The multiple shares are used to transfer the secret image by using the encryption and decryption process by means of the elliptic curve cryptography (ECC) technique. In ECC method, the public key is randomly generated in the encryption process and decryption process, the private key (H) is generated by utilizing the optimization technique and for evaluating the performance of the optimization by using the peak signal to noise ratio (PSNR). From the test results, the PSNR has been exposed to be 65.73057, also the mean square error (MSE) value is 0.017367 and the correlation coefficient (CC) is 1 for the decrypted image without any distortion of the original image and the optimal PSNR value is attained using the cuckoo search (CS) algorithm when compared with the existing works.

A new data hiding method based on chaos embedded genetic algorithm for color image

Abstract: Data hiding algorithms, which have many methods describing in the literature, are widely used in information security. In data hiding applications, optimization techniques are utilized in order to improve the success of algorithms. The genetic algorithm is one of the largely using heuristic optimization techniques in these applications. Long running time is a disadvantage of the genetic algorithm. In this paper, chaotic maps are used to improve the data hiding technique based on the genetic algorithm. Peak signal to noise ratio (PSNR) is chosen as the fitness function. Different sized secret data are embedded into the cover object using random function of MATLAB and chaotic maps. Randomness of genetic is performed by using different chaotic maps. The success of the proposed method is presented with comparative results. It is observed that gauss, logistic and tent maps are faster than random function for proposed data hiding method.

Image super-resolution reconstruction using the high-order derivative interpolation associated with fractional filter functions

Abstract: In this stydy, the authors present a single image super-resolution (SISR) reconstruction based on high-order derivative interpolation (HDI) in the fractional Fourier transform (FRFT) domain. First, the HDI formula is derived using a simple technique, which is based on the relationship between the fractional band-limited signal and the traditional band-limited signal. This interpolation formula contains the derivative information of the image and the FRFT domain filter functions (FDFF). Moreover, the advantages of the FDFF are also analysed. Second, the new SISR reconstruction is presented via the HDI. The main advantage is that the presented method involves the derivatives of an image in the resizing process. Moreover, the authors take advantage of the FDFF to resize the image. Furthermore, three evaluation criteria and some simulations are presented to validate the effectiveness of the proposed method. Last, the proposed method is applied to colour image processing. For a colour image case, the RGB colour space is chosen for super-resolution reconstruction. In addition to peak signal-to-noise ratio, the authors have also used the correlation to assess the quality of the reconstruction. Compared with many methods, extensive experimental results validate that the proposed method can obtain the better-edge characteristic, less blur and less aliasing.

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

Abstract: This paper proposes a multiclass support vector machine (SVM) based adaptive filter for removal of impulse noise from color images. The quality of the image gets degraded due to the presence of impulse noise. As a result, the homogeneity amongst the pixels gets distorted that needs to be restored. The feature set comprising of prediction error, difference between the median value and the center pixel; the median value in the kernel under operation has been used during this study. The pixel of test image is processed using adaptive window based filter that depends on the associated class assigned at the testing phase. The baseline system has been designed using modified histogram based fuzzy color filter (MHFC) technique. Four set of experiments have been carried out on a large database to validate the proposed method. The performance of the technique have been evaluated using peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). The results suggest that for fixed valued impulse noise, the proposed filter performs better than the MHFC in case of high density impulse noise (>45%). However, for random valued impulse noise the proposed filter outperforms the MHFC based method for both low and high density of noise. The objective analysis suggests that there is ~3dB improvement in PSNR as compared to the MHFC based method for high density of impulse noise. The results of SSIM along with visual observations indicate that the image details are maintained significantly in the proposed technique as compared to existing methods.

Probabilistic decision based filter to remove impulse noise using patch else trimmed median

Abstract: A new probabilistic decision based filter (PDBF) is presented to remove salt and pepper impulse noise in highly corrupted images. The filter employs two types of estimation techniques for denoising namely trimmed median (TM) and patch else trimmed median (PETM) which is our main contribution in this paper. Depending upon the estimated noise density, the filter utilizes either TM or PETM and hence enhanced outcome of denoising. Simulation results prove that the PDBF has outperformed recently proposed state-of-the-art filters in terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM), image enhancement factor (IEF), mean absolute error (MAE) and visual representation at the noise densities (ND) as high as 95%.

Quality metric-based fitness function for robust watermarking optimisation with Bees algorithm

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.

Performance of peak signal-to-noise ratio quality assessment in video streaming with packet losses

Abstract: The effect of packet losses over error-prone networks on visual quality of distributed video contents, estimated through subjective opinion and PSNR as an objective measure is analysed. It is shown that, within a fixed content, the variation of PSNR is a reliable indicator of the variation of packet loss rates. However, across different contents, the performance of PSNR is highly reduced. This performance drop can be corrected using the right pooling strategy.

Least Significant Bit and Discrete Wavelet Transform Algorithm Realization for Image Steganography Employing FPGA

Abstract: Steganography is the science that deals with conveying secret information by embedding into the cover object invisibly. In steganography, only the authorized party is aware of the existence of the hidden message to achieve secret communication. The image file is mostly used cover medium amongst various digital files such as image, text, audio and video. The proposed idea of this research work is to develop the robust image steganography. It is implemented using Least Significant Bit and Discrete Wavelet Transform techniques for digital image signal to improve the robustness & evaluate the performance of these algorithms. The parameters such as mean square error (MSE), bit error rate (BER), peak signal to noise ratio (PSNR) and processing time are considered here to evaluate the performance of the proposed work. In the proposed system, PSNR and MSE value ranges from 42 to 46 dB and 1.5 to 3.5 for LSB method respectively. For DWT method these results are further improved as it gives higher PSNR values between 49 to 57 dB and lower MSE values 0.2 to 0.7.

Colour image compression algorithm based on the DCT transform using difference lookup table

Abstract: A simple and efficient method for lossy colour image compression is proposed. Here, the discrete cosine transform (DCT) is applied to the YCbCr image obtained from the original RGB image. The bisection method is used to define the required threshold for a prefixed user peak signal to noise ratio as a controlled quality criterion. The thresholded and quantised DCT coefficients are encoded with a new technique. The proposed technique uses the difference of the indexes of the retained coefficients in coordination with DCT block adaptive scanning to encode efficiently the coefficients. The difference of the indexes is stored in a lookup table called (dLUT). When compared with recent methods, the obtained results show that the proposed algorithm achieves high performance.

Comparison of contrast enhancement techniques for medical image

Abstract: The main goal of this paper is to process a medical image. For a medical diagnosis, the result is more suitable. Contrast enhancement is used to improve the contrast of an image. Contrast enhancement of images is used for a different variety of applications such as in the medical field. Most of the images like medical images, remote sensing, aerial images and real life photographs suffer from poor contrast. The main goal of image enhancement is to improve the quality or clarity of images or to increase the interpretability in images for human viewing. In medical images detection and analysis, contrast enhancement techniques are one of the most significant stages. We are used contrast enhancement techniques to achieve contrast enhancement of images. The type of techniques includes neighborhood operation, average filter, bilateral ratinex, imadjust and sigmoid function. All these techniques are comparing with each other to achieve which enhancement techniques have produced a better contrast of an image. The four separate parameters are used. These parameters are such as peak signal to noise ratio (PSNR), mean square error (MSE), normalization coefficient (NC) and root mean square error (RMSE). In image research, this is one of the most important and difficult technique.

Optimization-based image watermarking with integrated quantization embedding in the wavelet-domain

Abstract: This study presents an optimization-based image watermarking scheme with integrated quantization embedding. First, peak signal to noise ratio (PSNR) is rewritten as a performance index in matrix form. In order to guarantee the robustness, this study embeds a watermark into the low-frequency coefficients of discrete wavelet transform (DWT). Unlike traditional way of single-coefficient quantization, this study applies amplitude quantization to embed the watermark and then rewrite this amplitude quantization as a constraint with embedding state. Then, an optimization-based equation connecting the performance index and amplitude-quantization constraint is obtained. Second, Lagrange Principle is used to solve the equation and then the optimal results are applied to embed the watermark. In detection, the hidden watermark can be extracted without original image. Finally, the performance of the proposed scheme is evaluated by PSNR and BER.

A novel spatial domain technique for digital image watermarking using block entropy

Abstract: During last few years for copyright protection, security and data authentication of digital media, digital watermarking has been raised as the one of the burning research topics due to the rapid expansion of Internet. This paper presents a new technique for image watermarking in the spatial domain where the concept of information theory is utilized with the popular LSB substation technique. Here, the cover image is segregated into a number of blocks and the watermark is embedded into the block(s) with the maximum entropy value. The extraction algorithm is also able to find the watermark correctly. The proposed algorithm was evaluated with the help of various standard performance measures like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) measure to verify the perceptibility and the robustness of the algorithm. Experimental resultsdemonstrate that the improved algorithm performs reasonably well over a large varied datasets of cover and watermark images.

Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising

Abstract: Digital images always inherit some extent of noise in them. This noise affects the information content of the image. Removal of this noise is very important to extract useful information from an image. However noise cannot be eliminated, it can only be minimized due to overlap between the signal and noise characteristics. This paper reviews image denoising algorithms which are based on wavelet, ridgelet, curvelet and contourlet transforms and benchmarks them based on the published results. This article presents the techniques, parameters used for benchmarking, denoising performance on standard images and a comparative analysis of the same. This paper highlights various trends in denoising techniques, based on which it has been concluded that a single parameter Peak Signal to Noise Ratio (PSNR) cannot exactly represent the denoising performance until other parameters are consistent. A new robust parameter Performance measure `P' is presented as a measure of denoising performance on the basis of a new concept named Noise Improvement Rectangle followed by its analysis. The results of the published algorithms are presented in tabular format in terms of PSNR and P which facilitates readers to have a bird's eye view of the research work in the field of image denoising and restoration.

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

Abstract: Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network FLANN and the Cat Swarm Optimization CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

Image Encryption Schemes : A Complete Survey

Abstract: Advancement in digital technologies has resulted in increased data transfer over internet in recent years. As a result, security of images/data is one of the biggest concern of many researchers. Therefore several cryptographic schemes have been proposed for image/data encryption. An efficient cryptographic scheme is one that have high brute force search time, low execution time complexity and should be able to provide good security. In this paper, several ciphers (traditional as well as modern) for images are compared based on various parameters such as: Time complexity, Peak Signal to Noise Ratio (PSNR), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI) and Entropy. In addition, the paper also shows the shortcomings of traditional ciphers that were used for text and how modern ciphers overcome this limitations. The analysis of simulation result shows that chaotic encryption schemes are most efficient and better than others.

Analysis of Particle Swarm Optimization Based 2D FIR Filter for Reduction of Additive and Multiplicative Noise in Images

Abstract: Noise in digital images is the major cause of severe artifacts. Filter design for denoising applications can also be addressed with optimization techniques as conventional filters incur in this. Exploration and Exploitation capability features of the Meta Heuristic Optimization Techniques make them applicable to noise reduction in digital images. An increasing number of Meta Heuristic Optimization algorithms make it suitable for designing FIR filters. In the proposed method, Particle Swarm Optimization, a global optimizer algorithm was used in calculating the appropriate coefficients for 2D FIR Filter. The proposed filter was applied to standard test images for testing its noise suppression capability. Indicators of performance, such as Peak signal to noise ratio (PSNR) values and Structural Content (SC) were used in accessing the efficiency of the proposed method and to the adaptability of the method for removing different noise types. Thus a brief comparison for noise suppression in digital images with both multiplicative and additive noise types using PSO optimized 2D FIR filter is addressed in this paper.

Reference Information Based Remote Sensing Image Reconstruction with Generalized Nonconvex Low-Rank Approximation

Abstract: Because of the contradiction between the spatial and temporal resolution of remote sensing images (RSI) and quality loss in the process of acquisition, it is of great significance to reconstruct RSI in remote sensing applications. Recent studies have demonstrated that reference image-based reconstruction methods have great potential for higher reconstruction performance, while lacking accuracy and quality of reconstruction. For this application, a new compressed sensing objective function incorporating a reference image as prior information is developed. We resort to the reference prior information inherent in interior and exterior data simultaneously to build a new generalized nonconvex low-rank approximation framework for RSI reconstruction. Specifically, the innovation of this paper consists of the following three respects: (1) we propose a nonconvex low-rank approximation for reconstructing RSI; (2) we inject reference prior information to overcome over smoothed edges and texture detail losses; (3) on this basis, we combine conjugate gradient algorithms and a single-value threshold (SVT) simultaneously to solve the proposed algorithm. The performance of the algorithm is evaluated both qualitatively and quantitatively. Experimental results demonstrate that the proposed algorithm improves several dBs in terms of peak signal to noise ratio (PSNR) and preserves image details significantly compared to most of the current approaches without reference images as priors. In addition, the generalized nonconvex low-rank approximation of our approach is naturally robust to noise, and therefore, the proposed algorithm can handle low resolution with noisy inputs in a more unified framework.

Image denoising using wavelet transform

Abstract: Image denoising is a noise removal technique used to remove noise from noisy image. The wavelet is one of the most popular techniques in recent developments in image denoising. It is effective in denoising because of its energy transformation ability to get wavelet coefficients. It is not possible to get noise suppression and characteristics preservation of the image at the same time. In this paper an improved method is presented by which the optimal threshold for every sub-band in neighboring window is determined by Stein’s Unbiased Risk Estimator (SURE). Then, the neigh shrink is applied in the neighboring window to get optimal PSNR (Peak Signal to Noise Ratio). The main aim of this research work is to increase the PSNR of an image while keeping the Mean Square Error (MSE) low. The algorithm was tested on various images and the results for different PSNR and MSE values are presented in this research paper.

Digital Image Watermarking using Discrete Wavelet Transform and Arithmetic Progression technique

Abstract: Nowadays it has become very easy to use digital information present over the internet because of the fast development in Information Technology sector. Digital Watermarking makes an attempt to resolve the problems associated with the management of property of media. It ends up in unauthorized copying and redistribution of digital content. In the proposed method, cover image is decomposed into low and high frequency components by the application of 1-level Discrete Wavelet Transform. Average of each subband is calculated. The watermark is embedded into the 1-level high-high, high-low, low-high subband of cover image using Arithmetic Progression technique. The subband which has the smallest average is to be embedded first. After that, the watermarked image is projected to several attacks like median filtering, JPEG compression, Gaussian low-pass filtering, shearing, cropping, rotation etc. with different distortion strengths. The watermark which is embedded in the middle frequency subbands and high frequency subband is taken out by similar mechanism. The imperceptibility and robustness of the watermarked image is checked out by measuring the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index values. From the implementation results, we came to know that this watermarking algorithm can withstand many image manipulations compared to other existing DWT based methods.

A Comprehensive Motion Estimation Technique for the Improvement of EIS Methods Based on the SURF Algorithm and Kalman Filter

Abstract: Video stabilization is an important technology for removing undesired motion in videos. This paper presents a comprehensive motion estimation method for electronic image stabilization techniques, integrating the speeded up robust features (SURF) algorithm, modified random sample consensus (RANSAC), and the Kalman filter, and also taking camera scaling and conventional camera translation and rotation into full consideration. Using SURF in sub-pixel space, feature points were located and then matched. The false matched points were removed by modified RANSAC. Global motion was estimated by using the feature points and modified cascading parameters, which reduced the accumulated errors in a series of frames and improved the peak signal to noise ratio (PSNR) by 8.2 dB. A specific Kalman filter model was established by considering the movement and scaling of scenes. Finally, video stabilization was achieved with filtered motion parameters using the modified adjacent frame compensation. The experimental results proved that the target images were stabilized even when the vibrating amplitudes of the video become increasingly large.

Estimation of the Image Quality under Different Distortions

Abstract: The evaluation of the image quality is very important. The best subjective evaluation of the image is done by the human eyes as they are the good receivers. Objective analysis of the image is done by using full reference metric. The results of the objective measurements are validated by the subjective measurements. The objective evaluation of image in this paper has been done using PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error) and SSIM (Structural Similarity Index Metric). These algorithms are applied on the different images. If the value of PSNR increases, the corresponding value of SSIM also increases and the value of MSE decreases. In the proposed wok, if the value of MSE increases by 8%, then the corresponding PSNR decreases by 10%. This is not applicable on all images because the MSE, PSNR and SSIM changes according to the complexity of the image. Every image has different coefficient of complexity. More complex image gets distorted first than the less complex image.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Abstract: Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures. Keywords: Ripplet, Directional discrete cosine transform (DDCT), Peak signal to noise ratio, MSE (mean square error), SC (structural content), MD (maximum difference), NCC (normalized cross correlation

A robust image watermarking using hybrid DCT and SLT

Abstract: Slantlet Transform (SLT) is an improved performance from Discrete Wavelet Transform (DWT). Based on characteristic on SLT; piecewise linear, two zero moments, and power quality for data compression. Those characteristic proves efficiency of SLT. One of the characteristic of SLT named power quality is better than Discrete Cosine Transform (DCT). DCT has a good signal compression. Their capability for embedding and resulting a robust image watermarking. In this paper, we proposed a hybrid technique in SLT-DCT. Our experimental result achieved high PSNR that proved good imperceptibility and robustness both in Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) that was tested in some image compression attack. This experiment has been done and yielded higher PSNR than previous research as compared in our results discussion. The experiments were performed using 5 pieces of gray scale image size of 512×512 jpg. The fifth image generating PSNR is greater than 40 dB. Barbara.jpg results the highest PNSR from of all the images that is 63.1920 dB and NC to attack JPEG compression is 1. This data proved that the proposed method produces robust image watermarking.