#Super Resolution

Discover how SRGAN can enhance image resolution using Generative Adversarial Networks. This method leverages TensorLayerX for significant improvements in image quality, supporting multiple frameworks including TensorFlow and PaddlePaddle with upcoming support for PyTorch. Train on datasets like DIV2K or use custom images, and evaluate results using the provided weights, facilitating superior image clarity for advancing computer vision tasks.

Explore this well-structured repository of key papers and codes from ECCV2024 and ECCV2020 that address various low-level vision tasks such as super-resolution, image de-raining, dehazing, and more. The collection supports the research community by encouraging contributions through stars, forks, and PRs, making it a valuable tool for advancing computer vision studies without biased promotion.

Explore an extensive collection of training and testing codes for leading image restoration models, including USRNet, DnCNN, FFDNet, SRMD, DPSR, MSRResNet, ESRGAN, BSRGAN, SwinIR, VRT, and RVRT. This repository offers capabilities for image enhancement tasks such as super-resolution, deblurring, and denoising leveraging diffusion models and deep learning frameworks. Stay informed with continuous updates and interactive demonstrations, and investigate the application of these methods in practical scenarios via differentiable programming interfaces. It serves as a valuable resource for researchers and developers in the field of visual computing.

This curated collection offers a detailed overview of significant research papers and code from CVPR conferences held in 2024, 2021, and 2020. It focuses on low-level vision tasks, including but not limited to super-resolution, de-raining, dehazing, deblurring, denoising, image restoration and enhancement, as well as inpainting and interpolation. Targeted at researchers and professionals in the field, the repository presents essential tools and knowledge for advancing image processing techniques. By engaging with the content through actions like starring or contributing, users can help drive collaborative development and progress within the community.