Abstract
Image contrast enhancement is a preprocessing phase that improves the performance of image processing applications such as pattern recognition and computer vision. Many images have poor quality due to low-luminance and low-contrast, which must be changed before further processing. It is significant for medical imaging because the low-light intensity makes it challenging to diagnose and analyze specific diseases accurately. In addition, when the depth information of a low-light image is unknown, the drawback of illumination enhancement becomes very challenging. Since Contrast enhancement of low-light images with non-uniform illumination is complex, it may lead to inefficient contrast enhancement methods. In other words, in such cases, if contrast enhancement methods are used to increase the contrast of dark areas, the bright regions become over-enhanced, which may lead to the disappearance of the details of these areas. Given this problem, in this paper, a new method is proposed to increase the image contrast that can improve the contrast in both areas of the image without adversely affecting the details of the image. The result represented the efficiency of proposed methods compared to other image enhancement methods based on full-reference and noreference metrics such as PSNR, MSE, NIQE, and BRISQUE.
Published Version
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