Abstract

Low-contrast images frequently arise due to poor illumination, limited sensor dynamic range, or unfavorable acquisition conditions, leading to compressed gray-level distributions and degraded visual quality. Although Histogram Equalization (HE) is widely used for contrast enhancement, it often results in over-enhancement, brightness distortion, and noise amplification, particularly in low-contrast scenarios. This paper presents a stepwise, threshold-centered intensity transformation for enhancing low-contrast images within a continuous gray-level framework, normalized to the interval [0,1]. The proposed method employs a threshold Tdetermined by the mean intensity of the input image, enabling adaptive localization of the dominant intensity level. The image variance is utilized to define the width of enhancement intervals around the threshold, while gain parameters regulate the amplification strength in a controlled and bounded manner. Experimental results demonstrate that the proposed method enhances contrast and preserves structural details while maintaining brightness consistency. Quantitative evaluations indicate superior performance compared to classical histogram equalization in terms of PSNR, SSIM, and AMBE, confirming the effectiveness and robustness of the proposed approach.

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