Advancements in Efficient Underwater Image Restoration Using ETransMapNet for Enhanced Dehazing - C. P. Indumathi; Haya Mesfer Alshahrani; N. A. Natraj; C. H. Sarada Devi (Tehnički Vjesnik)
Underwater (UW) information is essential for advancing human exploration and utilization of the underwater world, including fields such as UW Paleology, UW Target Detection, UW Object Tracking, UW Surveillance, and related activities. Visual media like movies and images enhance our natural understanding of underwater objectives. In the past decade, underwater photo restoration and enhancement have gained increasing attention. This study proposes a novel approach employing the recently developed Convolutional Neural Network (CNN) for dehazing, named ETransMapNet (Efficient Transmission Map Network). ETransMapNet is designed with convolution layers and nonlinear activations to execute four sequential processes: nonlinear regression, local maxima detection, multi-scale decomposition, and convolutional feature extraction. Unlike traditional CNNs, ETransMapNet replaces the initial layer's Rectified Linear Unit (ReLU) activation with a convolution layer utilizing a Maxout activation function. ETransMapNet extracts features using three convolution kernels of different sizes (3 × 3, 5 × 5, and 7 × 7). The method suppresses noise in the estimated transmittance map, while local extremum values maintain local consistency within the transmittance map. This study adopts Bilateral ReLU (BReLU) for normalizing network outputs within a 0 to 1 range. Additionally, Adaptive Bilateral Filtering (ABF) is applied to remove redundant artifacts from the predicted transmission map. White balancing addresses color divergence, and Laplacian pyramid fusion combines the color-corrected and dehazed images. In the final stage, the resultant image is transformed into the Wavelet and Directional Filter Banks (WDFB) domain for denoising and edge enhancement. Performance metrics reveal that the proposed ETransMapNet approach improves performance by 38% - 50% compared to previous methods.