AUTHOR=Han Lili , Liu Xiuping , Xu Tao , Wang Yuangan 

TITLE=Hybrid Mamba for amphibious Limulidae low-light image enhancement

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1578735

DOI=10.3389/fmars.2025.1578735

ISSN=2296-7745

ABSTRACT=Obtaining high-quality images of Limulidae in amphibious environments is a challenging task due to insufficient light and the complex optical properties of water, such as light absorption and scattering, which often result in low contrast, color distortion, and blurring. These issues severely impact applications like nocturnal biological monitoring, underwater archaeology, and resource exploration. Traditional image enhancement methods struggle with the complex degradation of such images, but recent advancements in deep learning have shown promise. This paper proposes a novel method for amphibious low-light image enhancement based on hybrid Mamba, which integrates wavelet transform, Discrete Cosine Transform (DCT), and Fast Fourier Transform (FFT) within the Mamba framework. Wavelet transform effectively decomposes images at multiple scales, capturing feature information at different frequencies and excelling in noise removal and detail preservation, whereas DCT concentrates and compresses image energy, aiding in the restoration of high-frequency components and improving clarity. FFT provides efficient frequency domain analysis, accurately locating key information in the image spectrum and enhancing image quality. Mamba, as an emerging optimization strategy, offers unique computational characteristics and optimization capabilities, making it well suited for this task. The main contributions include the construction of the amphibious low-light image dataset (ALID) in collaboration with the Beibu Gulf Key Laboratory of Marine Biodiversity Conservation and the introduction of the hybrid Mamba method. Extensive experiments on the ALID dataset demonstrate that our method outperforms state-of-the-art approaches in both subjective visual assessment and quantitative analysis, achieving superior results in brightness enhancement and detail reconstruction, thus paving new paths for amphibious low-light image processing and promoting further development in related industries and research.