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 5726–5750 of 7282 papers

TitleStatusHype
Color Image and Multispectral Image Denoising Using Block Diagonal Representation—0
Color image denoising by chromatic edges based vector valued diffusion—0
Soft-IntroVAE for Continuous Latent space Image Super-Resolution—0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models—0
Combined Approach for Image Segmentation—0
Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine—0
Combining Pre- and Post-Demosaicking Noise Removal for RAW Video—0
Cometh: A continuous-time discrete-state graph diffusion model—0
CommitBART: A Large Pre-trained Model for GitHub Commits—0
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement—0
CommUnet: U-net decoder for convolutional codes in communication—0
Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism—0
Community-Aware Graph Signal Processing—0
Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance—0
Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images—0
Comparative Performance Analysis of Image De-noising Techniques—0
Comparison of Autoencoders for tokenization of ASL datasets—0
Comparison of Generative Learning Methods for Turbulence Modeling—0
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising—0
Complex Deep Learning Models for Denoising of Human Heart ECG signals—0
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm—0
Complexity Scaling for Speech Denoising—0
Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation—0
ComposeAnything: Composite Object Priors for Text-to-Image Generation—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