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Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Computers, Materials & Continua 2024年第9期80卷 4687-4701页

作者： Congcong Ma Jiaqi Mi Wanlin Gao Sha Tao College of Information and Electrical Engineering China Agricultural UniversityBeijing100083China College of Artificial Intelligence Nankai UniversityTianjin300350China Key Laboratory of Agricultural Informatization Standardization Ministry of Agriculture and Rural AffairsChina Agricultural UniversityBeijing100083China

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 *** technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security *** significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still *** visual perception effect still needs further ***,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR *** designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale *** expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture ***,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed *** conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public *** 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.

关键词： Deep learning single image super-resolution artifacts texture details

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