The paper presents a multi-scale attention network (MAN) for efficient single image super-resolution (SISR). The key contributions are:
Multi-scale Large Kernel Attention (MLKA): The authors propose MLKA, which combines large kernel attention with multi-scale and gating mechanisms. MLKA can capture long-range dependencies at various granularity levels, aggregating global and local information while avoiding potential blocking artifacts.
Gated Spatial Attention Unit (GSAU): The authors integrate gate mechanism and spatial attention to construct a simplified feed-forward network, GSAU, which reduces parameters and computations compared to a multi-layer perceptron (MLP) while maintaining performance.
MAN Architecture: By stacking the proposed MLKA and GSAU modules, the authors develop the MAN family that can achieve varied trade-offs between model complexity and super-resolution performance. Experimental results show that MAN can perform on par with SwinIR while using fewer parameters and computations.
The paper first analyzes the limitations of existing ConvNet and transformer-based super-resolution models, then introduces the key components of MAN in detail. Extensive ablation studies are conducted to validate the effectiveness of each proposed module. Finally, MAN is compared with state-of-the-art classical and lightweight super-resolution methods, demonstrating its superior performance and efficiency.
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by Yan Wang,Yus... alle arxiv.org 04-16-2024
https://arxiv.org/pdf/2209.14145.pdfDomande più approfondite