JPEG Artifact Removal
10 papers with code • 0 benchmarks • 0 datasets
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Latest papers
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
We propose a practical approach to JPEG image decoding, utilizing a local implicit neural representation with continuous cosine formulation.
Controlling Vision-Language Models for Multi-Task Image Restoration
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.
Restore Anything Pipeline: Segment Anything Meets Image Restoration
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.
Removing Image Artifacts From Scratched Lens Protectors
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.
DriftRec: Adapting diffusion models to blind JPEG restoration
sp-uhh/driftrec
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In this work, we utilize the high-fidelity generation abilities of diffusion models to solve blind JPEG restoration at high compression levels.
Hypernetwork-Based Adaptive Image Restoration
ifryed/HyperRes
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Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model.
Towards Flexible Blind JPEG Artifacts Removal
jiaxi-jiang/fbcnn
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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.
Projected Distribution Loss for Image Enhancement
saurabh-kataria/projected-distribution-loss
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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.
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
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.
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network
The employment of convolutional neural networks has achieved unprecedented performance in the task of image restoration for a variety of degradation factors.
