Research on Improved Image Reconstruction Based on Residual Network

This paper addresses the limitations of image super-resolution reconstruction techniques, where traditional methods often struggle to maintain both sharp detail and natural texture when increasing image resolution, particularly when dealing with complex textures and edge information, leading to blur. We propose an improved Enhanced Deep Super-Resolution (EDSR) model that employs a deep residual network structure. This model stacks multiple residual blocks to build a deep network, effectively utilizing hierarchical information within images. Additionally, it incorporates sub-pixel convolution layers for upsampling, as well as multi-scale features and a global residual learning mechanism. During training, we utilize Adam optimizer, dynamic learning rate scheduling, and batch normalization techniques to ensure robust convergence and efficient learning in complex data distributions. Based on this foundation, we evaluate the improved EDSR model against the original EDSR and Super Resolution Generative Adversaria Network SRGAN models using various performance metrics. Experimental results demonstrate that the improved EDSR model achieves outstanding performance on the Flickr 1024 dataset at a scale factor of 4. Specifically, the unimproved EDSR (PSNR of 32.91dB, SSIM of 0.9581), SRGAN (despite potentially superior visual results, PSNR of 30.42dB, SSIM of 0.9727), and improved EDSR model (PSNR of 34.98dB, SSIM of 0.9828) not only surpass existing models in evaluation metrics but also strike an ideal balance between visual quality and computational efficiency. The enhanced EDSR algorithm improves the quality of image super-resolution reconstruction while optimizing computational efficiency. It provides a more efficient and accurate image upscaling solution for various applications including high-definition displays, medical imaging analysis, remote sensing image processing, and beyond.