DPSR
Image sharpener
A deep learning-based method to improve image quality by reducing blur effects
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)
839 stars
28 watching
211 forks
Language: Python
last commit: about 6 years agoblurry-imagesplug-and-playpytorch-implmentionsrresnetsuper-resolution
Related projects:
| Repository | Description | Stars |
|---|---|---|
| A deep learning-based approach to super-resolution of degraded images. | 1,222 | |
| Develops a single convolutional network to handle various image degradations with improved scalability and efficiency | 427 | |
| This project trains deep CNN denoisers to improve image restoration tasks such as deblurring and demosaicking through model-based optimization methods. | 602 | |
| Develops a deep learning-based method for deblurring images and videos from motion blur | 225 | |
| A deep learning model designed to progressively restore degraded images by iteratively refining the degradation and its representation in the image | 1,197 | |
| Unofficial PyTorch implementation of Zero-Shot Super Resolution using Deep Internal Learning from an image alone. | 201 | |
| Implementation of an algorithm for single image deblurring in images with defocus blur | 228 | |
| A PyTorch implementation of a deep learning model for super resolution | 194 | |
| A PyTorch implementation of blending images by optimizing a Poisson loss with style and content loss | 435 | |
| A software framework for single image deblurring using recursive kernels | 14 | |
| Restores degraded images by combining multiple tasks of dehazing, denoising and deraining in a single framework | 176 | |
| An implementation of a deep learning model for single image super-resolution using a generative adversarial network | 487 | |
| This project controls vision-language models to restore degraded images in various environments and conditions. | 673 | |
| Proposes an efficient neural architecture model for high-resolution image restoration tasks | 1,845 | |
| A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 |