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 2901–2925 of 7282 papers

TitleStatusHype
Cooperative Denoising for Distantly Supervised Relation Extraction—0
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration—0
Examining Joint Demosaicing and Denoising for Single-, Quad-, and Nona-Bayer Patterns—0
Exact Sparse Orthogonal Dictionary Learning—0
Cooling-Guide Diffusion Model for Battery Cell Arrangement—0
Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals—0
EWT: Efficient Wavelet-Transformer for Single Image Denoising—0
Few-Shot Concept Unlearning with Low Rank Adaptation—0
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology—0
6D-Diff: A Keypoint Diffusion Framework for 6D Object Pose Estimation—0
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy—0
Few-Shot Meta-Denoising—0
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer—0
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising—0
Convolution by Evolution: Differentiable Pattern Producing Networks—0
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism—0
Evolvable Conditional Diffusion—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
Filter Forests for Learning Data-Dependent Convolutional Kernels—0
A fast patch-dictionary method for whole image recovery—0
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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