Towards marine snow removal with fusing Fourier information

Marine snow, caused by the aggregation of small organic and inorganic particles, creates a visual effect similar to drifting snowflakes. Traditional methods for removing marine snow often use median filtering, which can blur the entire image. Although deep learning approaches attempt to address this issue, they typically only work in the spatial domain and still struggle with blurring and residual marine snow artifacts. These challenges arise because the spatial domain alone cannot easily distinguish between real object structures and noise-like marine snow artifacts. To address this, we propose the Deep Fourier Marine Snow Removal Network (DF-MSRN), which integrates both spatial and Fourier domain information to effectively restore images affected by marine snow. DF-MSRN employs a two-stage approach that leverages both Fourier frequency and spatial information: it first estimates a restored map of the amplitude component to address particle removal, avoiding additional noise in the spatial domain. Then, a fusion module combines Fourier frequency global information with spatial local information to refine image details. Experimental results show that DF-MSRN significantly outperforms existing denoising techniques on various marine image datasets, enhancing image clarity and detail preservation. • Proposed Fourier-based method utilizing unique Fourier traits for marine snow removal. • Developed a two-stage pipeline combining Fourier and spatial info for better removal. • Validated with extensive experiments, ablation studies, and benchmark comparisons.