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Found 6 papers, 4 papers with code
O-Mamba: O-shape State-Space Model for Underwater Image Enhancement
This branch includes MS-MoE for fusing multi-scale information within branches, Mutual Promotion module for interaction between spatial and channel information across branches, and Cyclic Multi-scale optimization strategy to maximize the use of multi-scale information.
Cycle Contrastive Adversarial Learning for Unsupervised image Deraining
However, these methods often struggle to generate high-quality, rain-free images due to a lack of attention to semantic representation and image content, resulting in ineffective separation of content from the rain layer.
Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal
Existing unsupervised methods have addressed the challenges of inconsistent paired data and tedious acquisition of ground-truth labels in shadow removal tasks.
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
PA-Diff consists of Physics Prior Generation (PPG) Branch, Implicit Neural Reconstruction (INR) Branch, and Physics-aware Diffusion Transformer (PDT) Branch.
Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration
Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks.
Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement
Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks.
