IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware Underwater Image Enhancement

TL;DR

This paper introduces an iterative framework for underwater image enhancement that identifies and addresses specific degradation conditions with dedicated deep networks, outperforming existing methods.

Contribution

It proposes a novel iterative framework that detects dominant degradation conditions and applies condition-specific enhancement networks, along with creating specialized datasets for training.

Findings

Outperforms nine baseline methods on UIEB and EUVP datasets.

Effectively identifies and resolves multiple degradation conditions.

Demonstrates the benefit of condition-specific enhancement over single-network approaches.

Abstract

Underwater image quality is affected by fluorescence, low illumination, absorption, and scattering. Recent works in underwater image enhancement have proposed different deep network architectures to handle these problems. Most of these works have proposed a single network to handle all the challenges. We believe that deep networks trained for specific conditions deliver better performance than a single network learned from all degradation cases. Accordingly, the first contribution of this work lies in the proposal of an iterative framework where a single dominant degradation condition is identified and resolved. This proposal considers the following eight degradation conditions -- low illumination, low contrast, haziness, blurred image, presence of noise and color imbalance in three different channels. A deep network is designed to identify the dominant degradation condition. Accordingly, an appropriate deep network is selected for degradation condition-specific enhancement. The second contribution of this work is the construction of degradation condition specific datasets from good quality images of two standard datasets (UIEB and EUVP). This dataset is used to learn the condition specific enhancement networks. The proposed approach is found to outperform nine baseline methods on UIEB and EUVP datasets.