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

Underwater image enhancement plays a pivotal role in addressing the challenges posed by the complex and dynamic underwater environment. While the previous research has conducted valuable explorations from a global enhancement perspective, underwater settings often exhibit multidistribution 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 multidistribution 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 among spatial frequency, receptive field, and image quality perception, and design a frequency-aware kernel selection convolution, which could adaptively select the size of convolutional 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 (GIB), whose neurons can comprehensively perceive global and local illumination through multiparameter correction representation, thereby guiding the focus of the enhancement work. Extensive experiments with the ablation analysis show the effectiveness of our proposed MDA-Net on four benchmark datasets: UFO-120, UIEB, UIEB-U60, and U45.