【论文笔记】(SR)《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》

《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》阅读笔记

problem
The lowresolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs

RCAN

  • a residual in residual (RIR) structure : form very deep network;allow abundant low-frequency information to be bypassed through multiple skip connections,making the main network focus on learning high-frequency information.
  • a cgannel attention mechanism : adaptively rescale channel-wise features by considering interdependencies among channels

【论文笔记】(SR)《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》_第1张图片

RCAN:G个RG
RG:B个RCAB
RCAB :
【论文笔记】(SR)《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》_第2张图片
【论文笔记】(SR)《Image Super-Resolution Using Very Deep Residual Channel Attention Networks》_第3张图片
。。

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