significant challenge in the field of digital image processing due to poor lighting conditions and uneven intensity distribution. This study aims to compare three contrast enhancement techniques Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to underwater imagery. The evaluation was conducted using quantitative metrics including entropy, contrast (RMS), and Structural Similarity Index (SSIM) to assess the improvement in image detail, intensity distribution, and structural similarity to the original image. Experimental results indicate that AHE achieves the highest entropy values, reflecting a significant enhancement of local information. HE provides the highest contrast values but tends to compromise the structural integrity of the image. CLAHE demonstrates the most balanced performance, producing the highest SSIM scores while maintaining stable enhancements in both contrast and detail. Based on these findings, CLAHE is recommended as the most effective contrast enhancement technique for underwater images, as it improves visual quality while preserving the original image structure. Key words : Underwater image enhancement; Contrast enhancement; CLAHE; HE; AHE.