Single Image Rain Streak Removal Using Harris Corner Loss and R-CBAM Network
This addresses the problem of rain removal in images for computer vision applications, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles single-image rain streak removal by proposing a network with Harris Corner Loss and R-CBAM to preserve details and improve restoration, achieving PSNR of 33.29 dB on Rain100L and 26.16 dB on Rain100H.
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.