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 5851–5875 of 7282 papers

TitleStatusHype
Convergent regularization in inverse problems and linear plug-and-play denoisers—0
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Convex Denoising using Non-Convex Tight Frame Regularization—0
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding—0
On Convex Duality in Linear Inverse Problems—0
Convex Optimization for Parallel Energy Minimization—0
Convex Regularization Behind Neural Reconstruction—0
Convex Variational Image Restoration with Histogram Priors—0
Convolutional Bipartite Attractor Networks—0
Convolutional Deep Denoising Autoencoders for Radio Astronomical Images—0
Convolutional Dictionary Regularizers for Tomographic Inversion—0
Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images—0
Convolutional Neural Networks Analyzed via Inverse Problem Theory and Sparse Representations—0
Convolutional Neural Networks Deceived by Visual Illusions—0
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets—0
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation—0
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging—0
Convolutional Sparse Coding with Overlapping Group Norms—0
Convolution by Evolution: Differentiable Pattern Producing Networks—0
Convolution Type of Metaplectic Cohen's Distribution Time-Frequency Analysis Theory, Method and Technology—0
Cooling-Guide Diffusion Model for Battery Cell Arrangement—0
Cooperative Denoising for Distantly Supervised Relation Extraction—0
Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication—0
CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation—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