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Image enhancement techniques for digital mammograms can preserve the contrast to distinguish the diagnostic features such as masses and microcalcifications. However, mammogram images suffer low contrast and high image noise due to low exposure radiation used. As a result, the diagnostic features are difficult to detect and analyse by the radiologist. In this article, we propose a novel Fuzzy Anisotropic Diffusion Histogram Equalisation Contrast Adaptive Limited (FADHECAL) enhancement technique to reduce the noise of the mammogram images while preserving contrast and brightness. The FADHECAL technique also applies Fuzzy Clipped Inference System (FCIS) which automatically select clip-limit during the enhancement process. The mammogram images were retrieved from Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) from the University of South Florida database. The results have shown that FADHECAL has the most superior results among other selected enhancement techniques with 6.502 ± 1.855 of AMBE, 0.934 ± 0.034 of SSIM, 15.742 ± 1.217 of MAE, 26.843 ± 2.541 of PSNR, 0.969 ± 0.021 of UIQI and 1.151 ± 0.147 of RMSE values. This FADHECAL can be used as an ideal platform of image enhancement for mammogram images to detect the breast cancer lesions with better noise reduction and preserving the image details. [ABSTRACT FROM AUTHOR]

Contrast and color are important attributes to extract and acquire much information from underwater images. However, normal underwater images contain bright foreground and dark background areas. Previous enhancement methods enhance the foreground areas but retain darkness and blue-green illumination of background areas. This study proposes a new method of enhancing underwater image, which is called recursive adaptive histogram modification (RAHIM), to modify image histograms column wisely in accordance with Rayleigh distribution. Modifying image saturation and brightness in the hue–saturation–value color model increases the natural impression of image color through the human visual system. Qualitative and quantitative evaluations prove the effectiveness of the proposed method. Comparison with state-of-the-art methods shows that the proposed method produces the highest average entropy, measure of enhancement (EME), and EME by entropy with the values of 7.618, 28.193, and 6.829, respectively. [ABSTRACT FROM AUTHOR]

The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance. [ABSTRACT FROM AUTHOR]

In this paper, we present a study of the usage of intensity exposure in histogram segmentation and its performance in histogram equalization. Two techniques are proposed: the mean-based bi-histogram equalization plateau limit (mean-BHEPL) or median-based BHEPL (median-BHEPL) and adaptive bi-histogram equalization algorithm (ABHE). Both techniques initially divide the input histogram into two subhistograms through a threshold value computed from the intensity exposure of the image. Histogram clipping for mean-BHEPL and median-BHEPL is then performed on these subhistograms using the mean and median values, respectively. Conventional histogram equalization is also implemented on each clipped subhistogram. The second proposed technique, ABHE, applies the modified version of the adaptive histogram equalization algorithm (AHEA) on both subhistograms. Results of extensive simulations reveal that mean-BHEPL and median-BHEPL perform comparably to the conventional BHEPL technique. ABHE exhibits excellent performance in image quality, naturalness, and mean brightness preservation. However, it is slightly inferior in image detail preservation to the conventional AHEA technique. In conclusion, segmenting the input histogram through the threshold value calculated based on the intensity exposure of the image yields good enhancement results. [ABSTRACT FROM AUTHOR]

The attenuation of the light that travels through a water medium subjects underwater images to several problems. As a result of low contrast and color performance, images are unclear and lose important information. Therefore, the objects in these images can hardly be differentiated from the background. This study proposes a new method called dual-image Rayleigh-stretched contrast-limited adaptive histogram specification, which integrates global and local contrast correction. The aims of the proposed method are to increase image details and to improve the visibility of underwater images while enhancing image contrasts. The two main steps of the proposed method are contrast and color corrections; an underwater image undergoes the former before the latter. Global contrast correction generates dual-intensity images, which are then integrated to produce contrast-enhanced resultant images. Subsequently, such images are processed locally to enhance details. The color of the images is also corrected to improve saturation and brightness. Qualitative and quantitative results show that the contrast of the resultant image improves significantly. Moreover, image detail and color are adequately enhanced; thus, the proposed approach outperforms current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A new approach based on Bi-Histogram Equalization is presented to enhance grayscale images. The proposed Adaptive Image Enhancement based on Bi-Histogram Equalization (AIEBHE) technique divides the input histogram into two sub-histograms, which are at the threshold of the histogram median for mean brightness preservation. Histogram clipping is performed to control the enhancement rate, and then the clipped sub-histograms are equalized and integrated to obtain the enhanced image. The novelty of AIEBHE is its flexibility in choosing the clipping limit that automatically selects the smallest value among histogram bins, mean, and median values, resulting in the conservation of a greater amount of information in the image. Automatic selection of the clipping limit addresses the issue of over-emphasizing of high frequency bins during histogram equalization. Simulation results reveal that AIEBHE technique outperforms other histogram-equalization-based enhancement techniques in terms of detail preservation and mean brightness preservation. [ABSTRACT FROM AUTHOR]