Abstract: Underwater images normally suffer from absorption and scattering effects of the light due to the oceanic environment. The key challenge in underwater imaging is object recognition due to the turbidity in water. Remotely operated vehicles provide artificial light which illuminates in a non-uniform way, resulting in poor visibility of underwater images. Contrast plays a major role in object recognition. Traditional methods deal with global information of an image and hence often does not achieve a good contrast enhancement. Underwater image characteristics are tentative and often change. Hence, there is a pressing need for adaptive algorithms in this area. Adaptive contrast enhancement algorithms based on the image fuzziness have been proposed for underwater images with varying contrast. Performance metrics like Peak Signal Noise Ratio (PSNR), Contrast to Noise Ratio (CNR), Absolute Mean Brightness Error (AMBE) and Image Enhancement Metric (IEM) are used to evaluate the performance of the proposed algorithm. Fuzzy edge retained amplification method provides enhancement with well-preserved edge information and improved contrast, when compared to the fuzzy amplification method. The proposed algorithm was able to achieve a better contrast for images that had 20% contrast with an AMBE of 36.57, IEM of 9.620, CNR of 11.39.