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

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Showing 651–675 of 7282 papers

TitleStatusHype
Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image EnhancementCode0
What Makes a Good Diffusion Planner for Decision Making?Code2
PET Image Denoising via Text-Guided Diffusion: Integrating Anatomical Priors through Text Prompts—0
DiffBrush:Just Painting the Art by Your Hands—0
Denoising bivariate signals via smoothing and polarization priors—0
Mobius: Text to Seamless Looping Video Generation via Latent ShiftCode2
MFSR: Multi-fractal Feature for Super-resolution Reconstruction with Fine Details Recovery—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud DenoisingCode1
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASRCode2
Finding Local Diffusion Schrödinger Bridge using Kolmogorov-Arnold NetworkCode0
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute—0
SubZero: Composing Subject, Style, and Action via Zero-Shot Personalization—0
BEVDiffuser: Plug-and-Play Diffusion Model for BEV Denoising with Ground-Truth Guidance—0
Spectral Analysis of Representational Similarity with Limited Neurons—0
Attention Distillation: A Unified Approach to Visual Characteristics TransferCode3
END: Early Noise Dropping for Efficient and Effective Context Denoising—0
A Dual-Purpose Framework for Backdoor Defense and Backdoor Amplification in Diffusion Models—0
Self-supervised conformal prediction for uncertainty quantification in Poisson imaging problems—0
On the Interpolation Effect of Score Smoothing—0
AKDT: Adaptive Kernel Dilation Transformer for Effective Image DenoisingCode1
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot ClassificationCode1
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—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