JPEG Artifact Removal

11 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Projected Distribution Loss for Image Enhancement

saurabh-kataria/projected-distribution-loss 16 Dec 2020

More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.

Towards Flexible Blind JPEG Artifacts Removal

jiaxi-jiang/fbcnn ICCV 2021

Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage.

Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network

6272code/6272-code 10 Jul 2019

The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.

Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable

Lillian1082/IRAE_pytorch 7 Sep 2020

Identifying the information lossless condition for deep neural architectures is important, because tasks such as image restoration require keep the detailed information of the input data as much as possible.

Hypernetwork-Based Adaptive Image Restoration

ifryed/HyperRes 13 Jun 2022

Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model.

DriftRec: Adapting diffusion models to blind JPEG restoration

sp-uhh/driftrec 12 Nov 2022

In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels.

Removing Image Artifacts From Scratched Lens Protectors

wyf0912/flare-removal 11 Feb 2023

Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts.

Restore Anything Pipeline: Segment Anything Meets Image Restoration

eth-siplab/rap 22 May 2023

In this paper, we introduce the Restore Anything Pipeline (RAP), a novel interactive and per-object level image restoration approach that incorporates a controllable model to generate different results that users may choose from.

Controlling Vision-Language Models for Multi-Task Image Restoration

algolzw/daclip-uir 2 Oct 2023

In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a multi-task framework for image restoration.

Bidirectional Consistency Models

Mosasaur5526/BCM-iCT-torch 26 Mar 2024

Interestingly, DMs can also invert an input image to noise by moving backward along the PF ODE, a key operation for downstream tasks such as interpolation and image editing.