Comparative Analysis of Advanced Local Laplacian Filtering Techniques for Underwater Image Enhancement

Underwater image enhancement function as a necessary procedure for marine biology research along with underwater exploration and several distinct professional fields. This research presents three advanced filtering methods developed from Local Laplacian Filtering: Local Laplacian Filtering with Standard Brightness (LLFSB) as well as Local Laplacian Filtering with Minimum Brightness (LLFMB) followed by Local Laplacian Filtering with Alpha Blending (LLFAB). The proposed image enhancement methods focus on correcting the three major problems which affect underwater image quality including uneven brightness levels and detail loss and perceptions degradation. The evaluation of proposed techniques uses five metrics to perform testing consisting of Absolute Mean Brightness Error (AMBE) and Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) as well as Natural Image Quality Evaluator (NIQE) and Perception-based Image Quality Evaluator (PIQE). The results show LLFAB provides better performance than both LLFSB and LLFMB in terms of brightness balance as well as structural preservation and perceptual quality. The adaptive alpha blending approach implemented in LLFAB creates a superior method to combat overexposure while improving subtle details while keeping imagery natural. While LLFSB and LLFMB show reliable performance in specific cases, LLFAB's robustness and consistency highlight its potential as a leading method for underwater image enhancement. This research emphasize the necessity of adaptive brightness blending which enhances underwater image quality while creating foundational work for additional research about its integration for diverse application needs.