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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 7282 papers

TitleStatusHype
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud CompletionCode1
Label-Noise Robust Diffusion ModelsCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
An Analysis and Implementation of the HDR+ Burst Denoising MethodCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and BeyondCode1
Latent-based Diffusion Model for Long-tailed RecognitionCode1
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising ModelsCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
Learning A Coarse-to-Fine Diffusion Transformer for Image RestorationCode1
Boosting of Implicit Neural Representation-based Image DenoiserCode1
Learning Deformable Kernels for Image and Video DenoisingCode1
Learning Enriched Features for Real Image Restoration and EnhancementCode1
Learning from Rules Generalizing Labeled ExemplarsCode1
Deterministic training of generative autoencoders using invertible layersCode1
Learning multi-scale local conditional probability models of imagesCode1
Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy ReconstructionCode1
Learning Self-prior for Mesh Denoising using Dual Graph Convolutional NetworksCode1
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion ProcessCode1
Boundary Guided Learning-Free Semantic Control with Diffusion ModelsCode1
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Show:102550
← PrevPage 39 of 292Next →
All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified