This paper proposes a very deep residual channel attention network (RCAN) for image super-resolution (SR). The RCAN combines a residual in residual (RIR) structure with a channel attention (CA) mechanism to achieve high accuracy and visual improvements. The RIR structure allows for very deep networks with long and short skip connections, enabling the network to bypass abundant low-frequency information and focus on learning high-frequency details. The CA mechanism adaptively rescales channel-wise features by considering interdependencies among channels, enhancing the network's discriminative ability. The RCAN is evaluated against state-of-the-art methods on various benchmark datasets, including Set5, Set14, B100, Urban100, and Manga109, using degradation models such as Bicubic (BI) and blur-downscale (BD). The results show that RCAN achieves superior performance in terms of PSNR and SSIM metrics, outperforming other methods. Additionally, the RCAN demonstrates strong performance in object recognition tasks, indicating its effectiveness in capturing detailed and informative features. The RCAN's RIR structure allows for very deep networks with over 400 layers, while the CA mechanism enhances the network's ability to focus on important features. The combination of these components enables the RCAN to achieve high accuracy and visual quality in image super-resolution. The paper also discusses the effectiveness of the RCAN in different degradation scenarios and highlights its potential for further research in deep learning for image super-resolution.This paper proposes a very deep residual channel attention network (RCAN) for image super-resolution (SR). The RCAN combines a residual in residual (RIR) structure with a channel attention (CA) mechanism to achieve high accuracy and visual improvements. The RIR structure allows for very deep networks with long and short skip connections, enabling the network to bypass abundant low-frequency information and focus on learning high-frequency details. The CA mechanism adaptively rescales channel-wise features by considering interdependencies among channels, enhancing the network's discriminative ability. The RCAN is evaluated against state-of-the-art methods on various benchmark datasets, including Set5, Set14, B100, Urban100, and Manga109, using degradation models such as Bicubic (BI) and blur-downscale (BD). The results show that RCAN achieves superior performance in terms of PSNR and SSIM metrics, outperforming other methods. Additionally, the RCAN demonstrates strong performance in object recognition tasks, indicating its effectiveness in capturing detailed and informative features. The RCAN's RIR structure allows for very deep networks with over 400 layers, while the CA mechanism enhances the network's ability to focus on important features. The combination of these components enables the RCAN to achieve high accuracy and visual quality in image super-resolution. The paper also discusses the effectiveness of the RCAN in different degradation scenarios and highlights its potential for further research in deep learning for image super-resolution.