用角点损失和注意力模块，精准修复带雨痕的单图。
Single Image Rain Streak Removal Using Harris Corner Loss and R-CBAM Network
- 引入角点损失，防止边界和纹理信息丢失。
- 在Rain100L上达33.29 dB，Rain100H上达26.16 dB。
- 适合图像去雨任务，尤其关注细节保留的研究者。
单张图像去雨不仅涉及噪声抑制，更需同时保留精细结构与整体视觉质量。本文提出一种新型图像恢复网络，通过引入角点损失（Corner Loss）有效约束恢复过程，防止物体边界与细节纹理在修复中丢失。此外，在编码器和解码器中引入残差卷积块注意力模块（R-CBAM），动态调整特征在空间与通道维度的重要性，使网络更聚焦于受雨痕严重干扰的区域。在Rain100L和Rain100H数据集上的定量评估表明，所提方法显著优于以往方法，分别取得33.29 dB和26.16 dB的PSNR。
原文摘要 · Abstract (English)
The problem of single-image rain streak removal goes beyond simple noise suppression, requiring the simultaneous preservation of fine structural details and overall visual quality. In this study, we propose a novel image restoration network that effectively constrains the restoration process by introducing a Corner Loss, which prevents the loss of object boundaries and detailed texture information during restoration. Furthermore, we propose a Residual Convolutional Block Attention Module (R-CBAM) Block into the encoder and decoder to dynamically adjust the importance of features in both spatial and channel dimensions, enabling the network to focus more effectively on regions heavily affected by rain streaks. Quantitative evaluations conducted on the Rain100L and Rain100H datasets demonstrate that the proposed method significantly outperforms previous approaches, achieving a PSNR of 33.29 dB on Rain100L and 26.16 dB on Rain100H.
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