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

Showing 15261550 of 7282 papers

TitleStatusHype
Progressive Correspondence Pruning by Consensus LearningCode1
Hyperspectral Image Denoising With Realistic DataCode1
An Unsupervised Deep Learning Approach for Real-World Image DenoisingCode1
NBNet: Noise Basis Learning for Image Denoising with Subspace ProjectionCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image RestorationCode1
Modeling Deep Learning Based Privacy Attacks on Physical MailCode1
Time-Travel RephotographyCode1
Memory AMPCode1
Visual Speech Enhancement Without A Real Visual StreamCode1
Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-EncodersCode1
Learning Medical Image Denoising with Deep Dynamic Residual Attention NetworkCode1
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure TimesCode1
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​Code1
Autoencoders that don't overfit towards the IdentityCode1
Pre-Trained Image Processing TransformerCode1
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for AutoencodersCode1
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication SystemsCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Unsupervised Deep Video DenoisingCode1
Rank-One Network: An Effective Framework for Image RestorationCode1
Legacy Photo Editing with Learned Noise PriorCode1
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric GuidanceCode1
Speech Denoising with Auditory ModelsCode1
Robust super-resolution depth imaging via a multi-feature fusion deep networkCode1
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging ProblemsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified