Over-enhancement Problem Research Articles

Image Enhancement using Recursive Separate Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

To improve image contrast, this paper introduces a recursive separate standard intensity deviation based clipped sub image histogram equalization method. This is an extension of standard intensity deviation value based sub image histogram equalization algorithm, in terms of histogram separation and equalization. In existing equalization methods do not effectively utilizes the information from different region in equalization process. In this scheme, the image histogram is bisected based on standard intensity deviation value. The further separation is carried out based on the specific region threshold value and the resulting four sub histograms are equalized individually. This is an effective method for enhancing, low exposure, medical and mammogram images and for addressing the over-enhancement problem. The performance evaluation of the proposed method is presented with the help of average information and visual quality assessment and the proposed algorithm outperforms existing recursive algorithms based on histogram equalization.

Image Enhancement using Recursive Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

The low exposure image enhancement has become indispensable inimage processing for better visibility. The most challenging in image enhancement is especially to curtail over-enhancement problems. This paper presents a method, performs the separation of the histogram based on respective standard intensity deviation value and then recursively equalizes all sub histograms independently. The over-enhancement problem is minimized by this method. It applies more in an underwater image, because of its low light conditions. The experiment results are analyzed in terms of entropy and output image inspection. The proposed method results show significant improvement over earlier recursive based histogram equalization algorithms.

Low‐light image enhancement with strong light weakening and bright halo suppressing

Low-light enhancement methods suffer from the over-enhancement problem which could induce the loss of the important texture and make images look unnatural. Moreover, some low-light images contain strong light areas that must be weakened to improve the visual effect. In this study, an enhancement method with strong light weakening and bright halo suppressing is presented. Firstly, the bright channel prior is applied to the inverted image to weaken the strong light both in and around the strong light areas. Then, a dehazing-type of algorithm with the dark channel prior is employed via superpixel segmentation to enhance the low-light image. Finally, a revised non-local denoising method is proposed to further refine the enhanced image. Experimental results showed that the proposed method achieved better visual effects compared with other state-of-the-art methods. Besides, the quantitative evaluation showed that the authors' method outperforms the other methods both in the aspect of enhancement and denoising.

Contrast Enhancement Algorithm Based on Gap Adjustment for Histogram Equalization

Image enhancement methods have been widely used to improve the visual effects of images. Owing to its simplicity and effectiveness histogram equalization (HE) is one of the methods used for enhancing image contrast. However, HE may result in over-enhancement and feature loss problems that lead to unnatural look and loss of details in the processed images. Researchers have proposed various HE-based methods to solve the over-enhancement problem; however, they have largely ignored the feature loss problem. Therefore, a contrast enhancement algorithm based on gap adjustment for histogram equalization (CegaHE) is proposed. It refers to a visual contrast enhancement algorithm based on histogram equalization (VCEA), which generates visually pleasing enhanced images, and improves the enhancement effects of VCEA. CegaHE adjusts the gaps between two gray values based on the adjustment equation, which takes the properties of human visual perception into consideration, to solve the over-enhancement problem. Besides, it also alleviates the feature loss problem and further enhances the textures in the dark regions of the images to improve the quality of the processed images for human visual perception. Experimental results demonstrate that CegaHE is a reliable method for contrast enhancement and that it significantly outperforms VCEA and other methods.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

Enriched Fuzzy and L*A*B based Mix Contrast Limited Adaptive Histogram Equilization

In this paper a novel algorithm has been designed to get rid of the problem of over-enhancement found in Mix CLAHE specifically for underwater images. The underwater image is suffering from low contrast and resolution because of dispossessed visibility circumstances, hence a subject identification become typical task. The processing of underwater image captured is important because the grade of underwater images distress and these images leads some serious problems in comparison with images from the clearer environment. This paper has proposed an hybrid approach which includes integrated the MIX-CLAHE with the L*A*B based fuzzy enhancement. The experimental results indicates that the proposed technique outperforms on the available methods.

Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference

In this paper, we propose a new sharpness enhancement algorithm for stereo images. Although the stereo image and its applications are becoming increasingly prevalent, there has been very limited research on specialized image enhancement solutions for stereo images. Recently, a binocular just-noticeable-difference (BJND) model that describes the sensitivity of the human visual system to luminance changes in stereo images has been presented. We introduce a novel application of the BJND model for the sharpness enhancement of stereo images. To this end, an overenhancement problem in the sharpness enhancement of stereo images is newly addressed, and an efficient solution for reducing the overenhancement is proposed. The solution is found within an optimization framework with additional constraint terms to suppress the unnecessary increase in luminance values. In addition, the reliability of the BJND model is taken into account by estimating the accuracy of stereo matching. Experimental results demonstrate that the proposed algorithm can provide sharpness-enhanced stereo images without producing excessive distortion.

Adaptive histogram equalization and its variations

Adaptive histogram equalization (ahe) is a contrast enhancement method designed to be broadly applicable and having demonstrated effectiveness. However, slow speed and the overenhancement of noise it produces in relatively homogeneous regions are two problems. We report algorithms designed to overcome these and other concerns. These algorithms include interpolated ahe, to speed up the method on general purpose computers; a version of interpolated ahe designed to run in a few seconds on feedback processors; a version of full ahe designed to run in under one second on custom VLSI hardware; weighted ahe, designed to improve the quality of the result by emphasizing pixels' contribution to the histogram in relation to their nearness to the result pixel; and clipped ahe, designed to overcome the problem of overenhancement of noise contrast. We conclude that clipped ahe should become a method of choice in medical imaging and probably also in other areas of digital imaging, and that clipped ahe can be made adequately fast to be routinely applied in the normal display sequence.