Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution (SISR) technology aims to reconstruct a clear, high-resolution image with more information from an input low-resolution image that is blurry and contains less information. This technology has significant research value and is widely used in fields such as medical imaging, satellite image processing, and security surveillance. Despite significant progress in existing research, challenges remain in reconstructing clear and complex texture details, with issues such as edge blurring and artifacts still present. The visual perception effect still needs further enhancement. Therefore, this study proposes a Pyramid Separable Channel Attention Network (PSCAN) for the SISR task. This method designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features. This expands the model's receptive field, reduces resolution loss, and enhances the model's ability to reconstruct texture details. Additionally, an innovative artifact loss function is designed to better distinguish between artifacts and real edge details, reducing artifacts in the reconstructed images. We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets. The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics, with improvements of 0.84 in Peak Signal-to-Noise Ratio (PSNR) and 0.017 in Structural Similarity Index (SSIM). This demonstrates that the method can effectively preserve high-frequency texture details, reduce artifacts, and have good generalization performance.