PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement

TL;DR

PixMamba introduces a dual-level architecture utilizing State Space Models to enhance underwater images efficiently, achieving state-of-the-art results by capturing global context and maintaining computational efficiency.

Contribution

The paper proposes a novel dual-level architecture with State Space Models for efficient global dependency modeling in underwater image enhancement.

Findings

Achieves state-of-the-art performance on multiple datasets.

Delivers visually superior underwater image enhancements.

Maintains computational efficiency compared to existing methods.

Abstract

Underwater Image Enhancement (UIE) is critical for marine research and exploration but hindered by complex color distortions and severe blurring. Recent deep learning-based methods have achieved remarkable results, yet these methods struggle with high computational costs and insufficient global modeling, resulting in locally under- or over- adjusted regions. We present PixMamba, a novel architecture, designed to overcome these challenges by leveraging State Space Models (SSMs) for efficient global dependency modeling. Unlike convolutional neural networks (CNNs) with limited receptive fields and transformer networks with high computational costs, PixMamba efficiently captures global contextual information while maintaining computational efficiency. Our dual-level strategy features the patch-level Efficient Mamba Net (EMNet) for reconstructing enhanced image feature and the pixel-level PixMamba Net (PixNet) to ensure fine-grained feature capturing and global consistency of enhanced image that were previously difficult to obtain. PixMamba achieves state-of-the-art performance across various underwater image datasets and delivers visually superior results. Code is available at: https://github.com/weitunglin/pixmamba.