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 51–75 of 7282 papers

TitleStatusHype
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
WISVA: Generative AI for 5G Network Optimization in Smart Warehouses—0
STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation—0
Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models—0
Imaging at the quantum limit with convolutional neural networksCode0
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
Sharpness-Aware Machine Unlearning—0
Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models—0
Discrete Diffusion in Large Language and Multimodal Models: A SurveyCode3
Evolvable Conditional Diffusion—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms—0
Block-wise Adaptive Caching for Accelerating Diffusion Policy—0
GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
DiffS-NOCS: 3D Point Cloud Reconstruction through Coloring Sketches to NOCS Maps Using Diffusion Models—0
Zero-shot denoising via neural compression: Theoretical and algorithmic frameworkCode0
Cross-Domain Conditional Diffusion Models for Time Series ImputationCode0
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering—0
CGVQM+D: Computer Graphics Video Quality Metric and DatasetCode2
DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning—0
Joint Denoising of Cryo-EM Projection Images using Polar Transformers—0
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Revisiting Transformers with Insights from Image Filtering—0
High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model—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