Underwater Image Restoration through Color Correction and UW-Net

Author:

Awan Hafiz Shakeel Ahmad1ORCID,Mahmood Muhammad Tariq1ORCID

Affiliation:

1. Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of Korea

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.

Funder

Education and Research Promotion Program of KoreaTech

Basic research program through the National Research Foundation (NRF) Korea

Korean government

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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