Underwater Image Restoration through Color Correction and UW-Net
Hafiz Shakeel Ahmad Awan, Muhammad Tariq Mahmood
Abstract
The restoration of underwater images plays a vital role in underwater target detection and recognition, underwater robots, underwater rescue, sea organism monitoring, marine geological surveys, and real-time navigation. In this paper, we propose an end-to-end neural network model, UW-Net, that leverages discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) for effective feature extraction for underwater image restoration. First, a color correction method is applied that compensates for color loss in the red and blue channels. Then, a U-Net based network that applies DWT for down-sampling and IDWT for up-sampling is designed for underwater image restoration. Additionally, a chromatic adaptation transform layer is added to the net to enhance the contrast and color in the restored image. The model is rigorously trained and evaluated using well-known datasets, demonstrating an enhanced performance compared with existing methods across various metrics in experimental evaluations.