Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
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Updated
Oct 9, 2021 - MATLAB
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
A Caffe-based implementation of very deep convolution network for image super-resolution
TOFlow: Video Enhancement with Task-Oriented Flow
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (CVPR, 2018) (Matlab)
The py version of toflow → https://github.com/anchen1011/toflow
Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)
Source code for our paper "Depth Super-Resolution Meets Uncalibrated Photometric Stereo"
Official MATLAB implementation of the "Sparse deconvolution" -v1.0.3
Super-resolution is a technique that constructs an high-resolution image from several observed low-resolution images.
Caffe implementation of "Two-Stage Convolutional Network for Image Super-Resolution" (ICPR 2018)
A code integration for light filed SR with paper in CVPRW2019
Source code of SACD(Super-resolution with Auto-Correlation two-step Deconvolution)
All released versions of SIMToolbox MATLAB codes
Neighborhood Regression for Edge-Preserving Image Super-Resolution (ICASSP 2015)
Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization (IEEE Transactions on Image Processing)
Super-resolution fluorescence microscopy by stepwise optical saturation
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