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 3151–3175 of 7282 papers

TitleStatusHype
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
Generative Joint Source-Channel Coding for Semantic Image Transmission—0
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application—0
Generative Lines Matching Models—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
Generative Model for Heterogeneous Inference—0
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding—0
Automated Channel Pruning with Learned Importance—0
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models—0
EraseNet: A Recurrent Residual Network for Supervised Document Cleaning—0
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar—0
Convex Denoising using Non-Convex Tight Frame Regularization—0
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction—0
Generative Models for Low-Dimensional Video Representation and Compressive Sensing—0
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior—0
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study—0
Generative Neural Fields by Mixtures of Neural Implicit Functions—0
Depth Completion Using a View-constrained Deep Prior—0
Generative Precipitation Downscaling using Score-based Diffusion with Wasserstein Regularization—0
Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion—0
High Noise Scheduling is a Must—0
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model—0
Application of Spherical Convolutional Neural Networks to Image Reconstruction and Denoising in Nuclear Medicine—0
Generative Recommendation with Continuous-Token Diffusion—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—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