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 2176–2200 of 7282 papers

TitleStatusHype
BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers—0
Denoising-based Contractive Imitation LearningCode0
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising—0
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising—0
Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model—0
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts—0
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos—0
SceneMI: Motion In-betweening for Modeling Human-Scene Interactions—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications—0
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization—0
SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models—0
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model—0
Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model—0
MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation—0
MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments—0
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging—0
Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided LearningCode0
Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity—0
PANDORA: Diffusion Policy Learning for Dexterous Robotic Piano Playing—0
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration—0
A Design of Denser-Graph-Frequency Graph Fourier Frames for Graph Signal Analysis—0
State Fourier Diffusion Language Model (SFDLM): A Scalable, Novel Iterative Approach to Language Modeling—0
Personalize Anything for Free with Diffusion Transformer—0
DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap—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