<![CDATA[Low-quality images, such as those resulting from digital capture under low-light conditions, present a significant challenge in the field of digital image processing. This study aims to enhance image visual quality using three contrast enhancement methods: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE). The dataset consists of 110 grayscale-converted street images captured under various lighting conditions (morning, noon, night, rainy, and clear weather). Evaluation was conducted using objective metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), execution time, and subjective assessment from 35 respondents. The results show that CLAHE consistently produces the best visual quality, achieving the highest PSNR of 12.93 dB and the lowest MSE of 3310.28 on a 32×32 grid, with an average execution time of 2–25 seconds. In comparison, HE recorded the lowest PSNR of 8.07 dB and the highest MSE of 10119.23, but had the fastest runtime of 0.3–0.4 seconds. AHE had the longest processing time, reaching up to 103 seconds, with inconsistent output quality. Based on user preference, 65% of respondents favored AHE, despite CLAHE being objectively superior. This study confirms CLAHE as the most effective method for enhancing image quality under extreme lighting conditions without sacrificing important visual details.]]>
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