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

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Showing 4751–4775 of 7282 papers

TitleStatusHype
Real Image Denoising With a Locally-Adaptive Bitonic Filter—0
Real Image Restoration via Structure-preserving Complementarity Attention—0
A Comprehensive Approach to Mode Clustering—0
Unpaired image denoising using a generative adversarial network in X-ray CT—0
Real-time and high-throughput Raman signal extraction and processing in CARS hyperspectral imaging—0
Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs—0
Diffusion Models for Wireless Communications—0
Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder—0
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model—0
Unraveling the Temporal Dynamics of the Unet in Diffusion Models—0
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder—0
Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network—0
Unrolled denoising networks provably learn optimal Bayesian inference—0
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation—0
Real-World Denoising via Diffusion Model—0
A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement—0
A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval—0
Recent progress in image denoising: A training strategy perspective—0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Reconciling "priors" & "priors" without prejudice?—0
Unrolling Nonconvex Graph Total Variation for Image Denoising—0
Reconstruct-and-Generate Diffusion Model for Detail-Preserving Image Denoising—0
Reconstruct Anything Model: a lightweight foundation model for computational imaging—0
Reconstructing the Noise Manifold for Image Denoising—0
Reconstructing the Noise Variance Manifold for Image Denoising—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