Channel Attention Enhanced Deep Residual Network for Single Image Super-Resolution

The growing need for high-resolution images has led to increasing interest in image super-resolution technology as a crucial computational technique for enhancing image quality. Deep learning has revolutionized image super-resolution, however many deep learning approaches lack flexibility in handling channel features, which can result in suboptimal processing of low and high-frequency information. To address this issue, this paper proposes a method that introduces multiscale convolution and channel attention mechanism based on an improved ResNet. By combining the multiscale convolution and channel attention mechanism with residual learning techniques, our network can effectively capture key image features, resulting in superior performance. The experimental results demonstrate that our network outperforms the original EDSR network with similar complexity, providing valuable insights for research in this field.