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Deblurring

57 papers with code · Computer Vision

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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.

#2 best model for Deblurring on REDS

DEBLURRING OBJECT DETECTION

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

7 May 2019xinntao/EDVR

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

DEBLURRING 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

Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring

CVPR 2017 SeungjunNah/DeepDeblur_release

To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear.

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).

DEBLURRING IMAGE DENOISING IMAGE RESTORATION

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

ICCV 2019 KupynOrest/DeblurGANv2

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.

DEBLURRING IMAGE RESTORATION

Neural Blind Deconvolution Using Deep Priors

6 Aug 2019csdwren/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