Deblurring

293 papers with code • 15 benchmarks • 13 datasets

Deblurring is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.

( Image credit: Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks )

Libraries

Use these libraries to find Deblurring models and implementations
3 papers
348
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Most implemented papers

Learning to See in the Dark

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

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

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

KupynOrest/DeblurGAN CVPR 2018

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

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

xinntao/EDVR 7 May 2019

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

Restormer: Efficient Transformer for High-Resolution Image Restoration

swz30/restormer CVPR 2022

Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.

Simple Baselines for Image Restoration

megvii-research/NAFNet 10 Apr 2022

Although there have been significant advances in the field of image restoration recently, the system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may hinder the convenient analysis and comparison of methods.

Multi-Stage Progressive Image Restoration

swz30/MPRNet CVPR 2021

At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.

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

KupynOrest/DeblurGANv2 ICCV 2019

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.

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

rozumden/DeFMO CVPR 2021

We propose a method that, given a single image with its estimated background, outputs the object's appearance and position in a series of sub-frames as if captured by a high-speed camera (i. e. temporal super-resolution).

Scale-recurrent Network for Deep Image Deblurring

jiangsutx/SRN-Deblur CVPR 2018

In single image deblurring, the "coarse-to-fine" scheme, i. e. gradually restoring the sharp image on different resolutions in a pyramid, is very successful in both traditional optimization-based methods and recent neural-network-based approaches.

Plug-and-Play Image Restoration with Deep Denoiser Prior

cszn/DPIR 31 Aug 2020

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.