Blind Underwater Image Restoration using Co-Operational Regressor Networks

TL;DR

This paper introduces Co-Operational Regressor Networks (CoRe-Nets), a novel deep learning model for underwater image restoration that outperforms existing methods in quality and efficiency by leveraging co-operating neural networks built on Self-ONNs.

Contribution

The paper proposes a new dual-network architecture, CoRe-Nets, combining an Apprentice and Master Regressor based on Self-ONNs for superior underwater image restoration.

Findings

Achieves state-of-the-art restoration performance on LSUI dataset.

Reduces computational complexity compared to existing methods.

Can surpass ground truth quality with a 2-pass process.

Abstract

The exploration of underwater environments is essential for applications such as biological research, archaeology, and infrastructure maintenanceHowever, underwater imaging is challenging due to the waters unique properties, including scattering, absorption, color distortion, and reduced visibility. To address such visual degradations, a variety of approaches have been proposed covering from basic signal processing methods to deep learning models; however, none of them has proven to be consistently successful. In this paper, we propose a novel machine learning model, Co-Operational Regressor Networks (CoRe-Nets), designed to achieve the best possible underwater image restoration. A CoRe-Net consists of two co-operating networks: the Apprentice Regressor (AR), responsible for image transformation, and the Master Regressor (MR), which evaluates the Peak Signal-to-Noise Ratio (PSNR) of the images generated by the AR and feeds it back to AR. CoRe-Nets are built on Self-Organized Operational Neural Networks (Self-ONNs), which offer a superior learning capability by modulating nonlinearity in kernel transformations. The effectiveness of the proposed model is demonstrated on the benchmark Large Scale Underwater Image (LSUI) dataset. Leveraging the joint learning capabilities of the two cooperating networks, the proposed model achieves the state-of-art restoration performance with significantly reduced computational complexity and often presents such results that can even surpass the visual quality of the ground truth with a 2-pass application. Our results and the optimized PyTorch implementation of the proposed approach are now publicly shared on GitHub.