SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

Showing 101125 of 7282 papers

TitleStatusHype
Denoising Vision TransformersCode3
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge DetectionCode3
Inversion-Free Image Editing with Language-Guided Diffusion ModelsCode3
DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic ModelsCode3
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?Code3
FreeU: Free Lunch in Diffusion U-NetCode3
HAT: Hybrid Attention Transformer for Image RestorationCode3
ModelScope Text-to-Video Technical ReportCode3
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesCode3
High-Resolution Image Reconstruction With Latent Diffusion Models From Human Brain ActivityCode3
On Distillation of Guided Diffusion ModelsCode3
ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-SpeechCode3
Planning with Diffusion for Flexible Behavior SynthesisCode3
Image Quality Assessment for Magnetic Resonance ImagingCode3
VRT: A Video Restoration TransformerCode3
MAXIM: Multi-Axis MLP for Image ProcessingCode3
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech ProcessingCode3
SwinIR: Image Restoration Using Swin TransformerCode3
Improved Denoising Diffusion Probabilistic ModelsCode3
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
Learning to See in the Extremely DarkCode2
CGVQM+D: Computer Graphics Video Quality Metric and DatasetCode2
Synthesis of discrete-continuous quantum circuits with multimodal diffusion modelsCode2
Optimal Density Functions for Weighted Convolution in Learning ModelsCode2
EasyText: Controllable Diffusion Transformer for Multilingual Text RenderingCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified