Image Restoration

184 papers with code • 0 benchmarks • 8 datasets

Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).

Source: Blind Image Restoration without Prior Knowledge

Greatest papers with code

Old Photo Restoration via Deep Latent Space Translation

microsoft/Bringing-Old-Photos-Back-to-Life 14 Sep 2020

Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.

Image Restoration Translation

Bringing Old Photos Back to Life

microsoft/Bringing-Old-Photos-Back-to-Life CVPR 2020

Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.

Image Restoration Translation

Deep Image Prior

DmitryUlyanov/deep-image-prior CVPR 2018

In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

Image Denoising Image Inpainting +3

Noise2Noise: Learning Image Restoration without Clean Data

NVlabs/noise2noise ICML 2018

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

Image Restoration Salt-And-Pepper Noise Removal

SwinIR: Image Restoration Using Swin Transformer

jingyunliang/swinir 23 Aug 2021

In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.

Color Image Denoising Image Denoising +4

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

kritiksoman/GIMP-ML 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.

Blind Face Restoration Image Restoration +1

EnlightenGAN: Deep Light Enhancement without Paired Supervision

kritiksoman/GIMP-ML 17 Jun 2019

Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?

Image Restoration Low-Light Image Enhancement

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

cszn/DPSR CVPR 2019

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 +1

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

titu1994/Image-Super-Resolution 29 Jun 2016

In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.

Image Denoising JPEG Artifact Correction +1

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.

Deblurring Image Denoising +2