MDA-Net: A Multidistribution Aware Network for Underwater Image Enhancement

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Abstract
Underwater image enhancement plays a pivotal role in addressing the challenges posed by the complex and dynamic underwater environment. While previous research has conducted valuable explorations from a global enhancement perspective, underwater settings often exhibit multi-distribution characteristics in both spatial frequency and illumination conditions that require specialized attention, that is, multiple spatial frequencies and lighting conditions coexist in the same image, making it difficult to achieve optimal enhancement using global mapping. To address this challenge, we propose a Multi-Distribution Aware Network (MDA-Net) that leverages local frequencies and illumination characteristics of images for adaptive adjustment to balance the diverse visual enhancement requirements of local regions. Specifically, to address the challenge of multiple spatial frequency distributions, we explore the correlation between spatial frequency, receptive field, and image quality perception, and design a Frequency-Aware Kernel Selection Convolution, which could adaptively select the size of convolution kernels based on the frequency complexity of each region, so as to balance the requirements of noise reduction and color fidelity in different regions. Furthermore, to address the challenge of multiple illumination distributions, we leverage the inherent illumination characteristics of the image to generate a Gamma Transformation-based Illumination Balancer, whose neurons can comprehensively perceive global and local illumination through multi-parameter correction representation, thereby guiding the focus of the enhancement work. Extensive experiments with ablation analysis show the effectiveness of our proposed MDA-Net on four benchmark datasets: UFO-120, UIEB, UIEB-U60 and U45.
Funding Information
  • National Natural Science Foundation of China (62271098, 62071081, 62471080)
  • Natural Science Foundation of Liaoning Province (2022-KF-12-10, 2024-MS-010)
  • Science and Technology Program of Liaoning Province (2022JH1/10800100, 2023JH2/101300199)
  • Dalian Science and Technology Innovation Foundation (2022JJ12WZ057, 2022JJ11CG002)
  • Fundamental Research Funds for the Central Universities (3132024240)

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