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 2651–2675 of 7282 papers

TitleStatusHype
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs—0
AstroClearNet: Deep image prior for multi-frame astronomical image restoration—0
Diffusion Models for Safety Validation of Autonomous Driving Systems—0
CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis—0
A statistical physics framework for optimal learning—0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—0
A Statistical Approach to Signal Denoising Based on Data-driven Multiscale Representation—0
A General Framework for Fast Stagewise Algorithms—0
Cross-Patch Graph Convolutional Network for Image Denoising—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis—0
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training—0
Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study—0
Assistive Recipe Editing through Critiquing—0
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models—0
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation—0
Filter characteristics in image decomposition with singular spectrum analysis—0
Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation—0
Cross-Image Attention for Zero-Shot Appearance Transfer—0
Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters—0
Assessing Wireless Sensing Potential with Large Intelligent Surfaces—0
Active Learning and Best-Response Dynamics—0
Cross-domain Recommendation via Deep Domain Adaptation—0
Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck—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