<p>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 and SSIM of 0.954 on Rain100L, and a PSNR of 26.16 dB and SSIM of 0.854 on Rain100H. In particular, additional ablation studies confirmed that the introduction of Harris Corner Loss plays a critical role in enhancing restoration quality, ensuring both structural consistency and fine detail preservation even in complex rain streak scenarios. Moreover, compared to other existing methods, it demonstrates a noticeably superior improvement in terms of structural consistency and fine detail preservation, further highlighting the effectiveness of the proposed approach. These experimental results provide clear evidence of the robustness and effectiveness of the proposed method. This research presents a new approach that comprehensively addresses both noise removal and structural preservation, offering high scalability and practicality as a fundamental technology applicable to a wide range of future image restoration and enhancement tasks.</p>