Bézier curve-based saturation-aided optimal brightness adjustment for dark image clearness enhancement with image fusion

P‐5.6: Synergistic Low‐Light Image Enhancement: A Fusion of Dark Channel Dehazing and K‐means Clustering

“…Lu's [5] 28.33 [4] accurately reproduces the colors of most color blocks but exhibits noticeable noise at the brightness transitions. The algorithm in reference [5] achieves relatively uniform local brightness and high color fidelity but introduces noticeable haze-like artifacts when enhancing grayscale color blocks.…”

“…Lu's [5] 28.33 [4] accurately reproduces the colors of most color blocks but exhibits noticeable noise at the brightness transitions. The algorithm in reference [5] achieves relatively uniform local brightness and high color fidelity but introduces noticeable haze-like artifacts when enhancing grayscale color blocks. In contrast, the algorithm proposed in this paper maintains color fidelity while exhibiting fewer noise and haze-like artifacts at the perceptual level, leading to a superior subjective evaluation.…”

“…Despite the marginally increased computational demands compared to traditional algorithms, the CUDCP's enhanced capability in improving underwater image clarity is evident. In a different vein, Zhichun Lu and her team have innovated a dark image enhancement technique that integrates Bezier curves with saturation supplementation [5] . This algorithm, through meticulous luminance estimation and luminance adjustment, enriches image chromaticity and diversity while proficiently mitigating issues pertaining to overexposure and detail loss, albeit untested on highly dim images.…”