Showing papers on "Histogram equalization published in 2022"
TL;DR: The algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of theimage.
Abstract: In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.
45 citations
TL;DR: In this article , a cascade convolutional neural network (CNN) is employed for nerve optic segmentation by two distinct input images, and the final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with other methods.
Abstract: Abstract The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.
29 citations
TL;DR: In this paper , a cascade convolutional neural network (CNN) is employed for nerve optic segmentation by two distinct input images, and the final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with other methods.
Abstract: Abstract The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.
18 citations
TL;DR: This research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.
Abstract: Background The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. Methods This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. Results We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. Conclusion Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.
16 citations
16 citations
TL;DR: In this paper , a spatially adaptive multi-scale image enhancement (SAMSIE) scheme is proposed, which decomposes a low-contrast image into multiscale layers.
16 citations
TL;DR: Wang et al. as mentioned in this paper proposed an effective shifted-phase histogram equalization (SHE) method for nonlinear correction based on the finding that the distorted histogram contains three symmetric valleys in theory.
Abstract: Fringe projection profilometry has been widely used for 3-D measurement, but the gamma nonlinearity severely affects the accuracy. Ideally, the histogram of the undistorted phase is uniform, but that of the distorted phase becomes nonuniform. In practice, adequate sampling pixels are required to calculate the phase histogram from modulated fringe patterns. Therefore, this article proposes an effective shifted-phase histogram equalization (SHE) method for nonlinear correction. Based on the finding that the distorted histogram contains three symmetric valleys in theory, if we shift the wrapped phase by $\pm 2\pi $ /3, the shifted phases will share the same histogram as the original phase. Without additional patterns, we can easily obtain two shifted phases. Combining the original and two shifted phases, the number of sampling pixels will increase by three times, and we can obtain more accurate histogram than the original phase. To make the histogram of the distorted phase be uniform as that of the undistorted phase, we directly apply histogram equalization on the distorted phase to estimate the undistorted phase. Moreover, this article proposes a spline interpolation optimization (SIO) method to eliminate the discretization error caused by histogram equalization. Simulations and experiments have been carried out, and their results indicate that the proposed SHE method performs better than the conventional phase histogram equalization (PHE) method, and the proposed SIO method can further reduce the discretization error.
14 citations
TL;DR: In this article , the effect of image enhancement algorithms such as Laplace transform, Wavelet transforms, Adaptive gamma correction and Contrast limited adaptive histogram equalization (CLAHE) on Chest CT images for the classification of Covid-19 using the EfficientNet algorithm was explored.
14 citations
TL;DR: In this article , an interval-valued intuitionistic fuzzy sets (IVIFS) based on fuzzy sets constructed from fuzzy sets are used to enhance images taken in low-light.
Abstract: This work introduces a program to enhance images taken in low-light. Fuzzy set theory is creating a significant shift in image processing. Interval-valued intuitionistic fuzzy sets (IVIFS) based on intuitionistic fuzzy sets constructed from fuzzy sets are used to enhance images taken in low-light. In the proposed method, first the given low-light image is fuzzified by normal fuzzification. Then the fuzzified image is converted to an interval-valued intuitionistic fuzzy image. This image will be proposed enhanced image after applying the contrast limited adaptive histogram equalization (CLAHE). The experimental results reveal that the proposed method gives better results when compared with other existing methods like histogram equalization (HE), CLAHE, brightness preserving dynamic fuzzy histogram equalization (BPDFHE), histogram specification approach (HSA). Based on the performance analysis like entropy and correlation coefficient (CC), the proposed method gives better results.
13 citations
TL;DR: Wang et al. as discussed by the authors proposed an underwater image enhancement method via integrated RGB and LAB color models (RLCM), wherein the local contrast of the L channel is enhanced by a histogram with local enhancement and exposure cut-off strategy, whereas the difference between the A and B channels is traded-off by a gain equalization strategy.
Abstract: Images taken underwater suffers from color shift and poor visibility because the light is absorbed and scattered when it travels through water. To handle the issues mentioned above, we propose an underwater image enhancement method via integrated RGB and LAB color models (RLCM). In the RGB color model, we first fully consider the leading causes of underwater image color shift, and then the poor color channels are corrected by dedicated fractions, which are designed via calculating the differences between the well and poor color channels. In the LAB color model, wherein the local contrast of the L channel is enhanced by a histogram with local enhancement and exposure cut-off strategy, whereas the difference between the A and B channels is traded-off by a gain equalization strategy. Besides, a normalized guided filtering strategy is incorporated into the histogram enhancement process to mitigate the effects of noise. Ultimately, the image is inverted from the LAB color model to the RGB color model, and a detail sharpening strategy is implemented in each channel to obtain a high-quality underwater image. Experiments on various real-world underwater images demonstrate that our method outputs better results with natural color and high visibility. • A dedicated fractions-based method to tackle the color shifts of underwater images. • A local enhancement and exposure cut-off-based histogram to improve local contrast. • A gain equalization method to trade-off the differences between the A and B channels. • A detail sharpening method to improve the details and edges of the output image.
13 citations
TL;DR: Wang et al. as discussed by the authors proposed a two-dimensional histogram equalization to improve the contrast of an image based on its nature, which takes advantage of the joint occurrences of edge information and pixel intensities in the low contrast image.
Abstract: The histogram equalization approach, which is employed for image enhancement, reduces the number of pixel intensities, resulting in detail loss and an unnatural impression. This research proposes a strategy to improve the contrast of an image based on its nature. The images' statistical parameters mean, median and kurtosis are extracted and utilized to classify them into uniform and non-uniform background images. Initially, the image is decomposed using a multilevel decomposition based on the l1−l0 minimization model to extract its significant edge information. Later, the retrieved edge information is employed in proper histogram equalization to produce an improved result. Variational histogram equalization is proposed here to overcome the problem of over-amplification and artifacts in the homogeneous zone caused by histogram spikes in the uniform background images. Non-uniform background images are enhanced via two-dimensional histogram equalization, which takes advantage of the joint occurrences of edge information and pixel intensities in the low contrast image. The proposed technique is tested on the five databases: CSIQ, TID2013, LOL, DRESDEN, and FLICKR. SD, CII, DE, NIQE, and AMBE are the performance metrics used to validate the algorithm's effectiveness. Experimental analysis shows that the proposed technique outperforms the other algorithms, including deep learning architectures in high CII, SD, DE, and low NIQE values.
TL;DR: Comparison results show that the color correction and detail enhancement by the proposed method are superior than previous deep learning models and traditional methods.
Abstract: Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.
TL;DR: In this article , a tripartite sub-image histogram equalization method is proposed to enhance slightly low contrast gray-tone images, which is a less explored area in the literature.
TL;DR: In this paper , an enhancement method based on GAN (Generative Adversarial Network) was proposed to improve low-light image enhancement algorithms, aiming to improve the quality of lowlight images by studying some technical means and methods.
Abstract: Deep underwater color images have problems such as low brightness, poor contrast, and loss of local details. In order to effectively enhance low-quality underwater images, this paper proposes an enhancement method based on GAN (Generative Adversarial Network). This paper studies low-light image enhancement algorithms, aiming to improve the quality of low-light images by studying some technical means and methods, and restore the original scene information of low-quality images, so as to obtain natural and clear images with complete details and structural information. In order to verify the effectiveness of this method, image databases such as DIARETDB0 and SID are used as the research object, combined with multi-scale Retinex color reproduction contrast-constrained adaptive histogram equalization to compare the performance of the enhanced algorithm. The results show that the processed image is better than other image enhancement methods in terms of color protection, contrast enhancement, and image detail enhancement. The proposed method significantly improves the indicators proposed in the article.
TL;DR: The portrait was actualized for CAD utilizing image processing techniques with deep learning, initially to perceive a brain tumor in a person with early signs of brain tumor, and achieves an accuracy of 93%, precision of 92%, and 94% of specificity.
TL;DR: The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.
Abstract: In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.
TL;DR: In this article , a local entropy mapping histogram is built through the modified sigmoid function to describe the detailed distribution of the infrared image, and then the LOESS algorithm and local minimum examination are used to adaptively segment the LEM histogram into multiple sub-histograms.
TL;DR: In this article , a joint histogram equalization (JHE) based technique is proposed to utilize the information among each pixel and its neighbors, which improves the contrast of an image.
TL;DR: In this paper, a novel enhancement method is proposed to improve the luminosity and contrast of the color retinal image, which is applied to the publicly available structured analysis of the retina (STARE) image dataset.
TL;DR: An unsupervised approach for vessels segmentation out of retinal images using Contrast Limited Histogram Equalization as well as Fuzzy Histogram Based Equalization for contrast enhancement is suggested.
Abstract: Fundus imaging is one of the crucial methods that help ophthalmologists for diagnosing the various eye diseases in modern medicine. An accurate vessel segmentation method can be a convenient tool to foresee and analyze fatal diseases, including hypertension or diabetes, which damage the retinal vessel’s appearance. This work suggests an unsupervised approach for vessels segmentation out of retinal images. The proposed method includes multiple steps. Firstly, from the colored retinal image, green channel is extracted and preprocessed utilizing Contrast Limited Histogram Equalization as well as Fuzzy Histogram Based Equalization for contrast enhancement. To expel geometrical articles (macula, optic disk) and noise, top-hat morphological operations are used. On the resulted enhanced image, matched filter and Gabor wavelet filter are applied, and the outputs from both is added to extract vessels pixels. The resulting image with the now noticeable blood vessel is binarized using human visual system (HVS). A final image of segmented blood vessel is obtained by applying post-processing. The suggested method is assessed on two public datasets (DRIVE and STARE) and showed comparable results with regard to sensitivity, specificity and accuracy. The results we achieved with respect to sensitivity, specificity together with accuracy on DRIVE database are 0.7271, 0.9798 and 0.9573, and on STARE database these are 0.7164, 0.9760, and 0.9560, respectively, in less than 3.17 s on average per image.
TL;DR: An image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process is proposed.
Abstract: In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms.
TL;DR: In this article , a novel enhancement method is proposed to improve the luminosity and contrast of the color retinal image, which is tested on publicly available structured analysis of the retina (STARE) image dataset, and promising results are obtained.
TL;DR: In this paper , an underwater image enhancement method that includes color correction, color constancy, multi-scale retinex (MSR), edge-aware and texture smoothing filters, and contrast stretching was proposed.
TL;DR: Wang et al. as discussed by the authors proposed a 2D histogram based contrast enhancement with reversible data hiding (CE-RDH) scheme by taking brightness preservation (BP) into account, which preserves image color and brightness while achieving better image quality than the existing schemes.
Abstract: Recently, contrast enhancement with reversible data hiding (CE-RDH) has been proposed for digital images to hide useful data into contrast-enhanced images. In existing schemes, one-dimensional (1D) or two-dimensional (2D) histogram is equalized during the process of CE-RDH so that an original image can be exactly recovered from its contrast-enhanced version. However, noticeable brightness change and color distortion may be introduced by applying these schemes, especially in the case of over enhancement. To preserve image quality, this paper presents a new 2D histogram based CE-RDH scheme by taking brightness preservation (BP) into account. In particular, the row or column of histogram bins with the maximum total height are chosen to be expanded at each time of histogram modification, while the row or column of bins to be expanded next are adaptively chosen according to the change of image brightness. Experimental results on three color image sets demonstrate efficacy and reversibility of the proposed scheme. Compared with the schemes using 1D histogram, image brightness can be preserved more finely by modifying the generated 2D histogram. Moreover, our proposed scheme preserves image color and brightness while achieving better image quality than the existing schemes.
TL;DR: In this paper, the fuzzy logic technique has been used to restore the distorted tribal artworks and the results obtained by this process have been compared with different histogram equalization (HE) techniques.
TL;DR: In this article , the fuzzy logic technique has been used to restore the distorted tribal artworks and the results obtained by this process have been compared with different histogram equalization (HE) techniques.
TL;DR: In this paper , the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach, which can be used for surface evaluation for finish turning of the Nimonic263 alloy.
Abstract: Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.
01 Jan 2022
TL;DR: CLAHE and guided-filtering-based image defogging algorithm is presented, which has huge application in various areas like tracking, navigation, surveillance, visual-based security, etc.
Abstract: Due to some undesired particles in the atmosphere such as haze, fog, mist, etc., the image quality, taken outside, can suffer from poor visibility. To reestablish the original quality of the image, degraded by the presence of fog, the fog removal algorithm is of significance. Fog removal algorithm has huge application in various areas like tracking, navigation, surveillance, visual-based security, etc. In this work, we present CLAHE and guided-filtering-based image defogging algorithm.