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Greatest papers with code

Learning to See in the Dark

CVPR 2018 cchen156/Learning-to-See-in-the-Dark

Imaging in low light is challenging due to low photon count and low SNR.

DEBLURRING DENOISING

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

CVPR 2018 KupynOrest/DeblurGAN

The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images.

DEBLURRING OBJECT DETECTION

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

7 May 2019open-mmlab/mmsr

In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.

 Ranked #1 on Deblurring on REDS

DEBLURRING VIDEO RESTORATION VIDEO SUPER-RESOLUTION

Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels

CVPR 2019 cszn/DPSR

In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels.

DEBLURRING IMAGE RESTORATION IMAGE SUPER-RESOLUTION

GIMP-ML: Python Plugins for using Computer Vision Models in GIMP

27 Apr 2020kritiksoman/GIMP-ML

Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.

COLORIZATION DEBLURRING DENOISING IMAGE INPAINTING IMAGE MANIPULATION IMAGE SUPER-RESOLUTION MONOCULAR DEPTH ESTIMATION SEMANTIC SEGMENTATION SINGLE IMAGE DEHAZING VIDEO FRAME INTERPOLATION

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

ICCV 2019 kritiksoman/GIMP-ML

We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility.

Ranked #14 on Deblurring on GoPro (using extra training data)

IMAGE RESTORATION SINGLE-IMAGE BLIND DEBLURRING

Learning Deep CNN Denoiser Prior for Image Restoration

CVPR 2017 cszn/ircnn

Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).

COLOR IMAGE DENOISING DEBLURRING IMAGE DENOISING IMAGE RESTORATION

Neural Blind Deconvolution Using Deep Priors

CVPR 2020 csdwren/SelfDeblur

To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.

DEBLURRING SELF-SUPERVISED LEARNING

Deep Video Deblurring for Hand-Held Cameras

CVPR 2017 shuochsu/DeepVideoDeblurring

We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

DEBLURRING SCENE UNDERSTANDING