INFWIDE
Deblurring method
A deep learning-based image deblurring method designed to handle low-light conditions
INFWIDE: Image and Feature Space Deep Wiener Deconvolution for Non-blind Image Deblurring in Low-Light Conditions
13 stars
1 watching
1 forks
Language: Python
last commit: almost 2 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| This project develops a deep learning-based image deblurring algorithm using iterative upsampling network architecture | 164 |
| Develops a deep learning-based method for deblurring images and videos from motion blur | 225 |
| An image deblurring algorithm that leverages flow-based motion prior and kernel estimation for blind image restoration. | 29 |
| An image deblurring technique based on frequency selection using machine learning models | 18 |
| A deep learning-based image deblurring system designed to remove blur from dynamic scenes. | 691 |
| A Python implementation of an image deblurring technique based on backprojection from a deep learning prior | 6 |
| Deblurring algorithm for videos using a neural network | 52 |
| Restores video frames to sharp clarity by predicting sharper central images from blurry input. | 192 |
| Develops a deep learning-based method to detect and remove defocus blur from images | 17 |
| An algorithmic approach to improve single image motion deblurring by adapting to local variations in the input image | 3 |
| Develops algorithms to restore sharp images from blurry ones and interpolate missing frames in video sequences with improved accuracy | 81 |
| Deblurring technique developed using machine learning and signal processing algorithms to restore images from blurry conditions. | 20 |
| An efficient RNN-based model for real-world video deblurring with a large-scale dataset and pre-trained models. | 322 |
| Deblurring technique using neural radiance fields with physical scene priors | 83 |
| A dataset and algorithm for deblurring images of moving scenes, specifically designed to handle dynamic blurs caused by camera movement and object motion. | 72 |