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 20762100 of 7282 papers

TitleStatusHype
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
Layered Rendering Diffusion Model for Controllable Zero-Shot Image SynthesisCode0
Lateral Connections in Denoising Autoencoders Support Supervised LearningCode0
DeepISP: Towards Learning an End-to-End Image Processing PipelineCode0
Automatic Online Multi-Source Domain AdaptationCode0
Unsupervised dynamic modeling of medical image transformationCode0
Educating Text Autoencoders: Latent Representation Guidance via DenoisingCode0
Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace ModelsCode0
Learned Convolutional Sparse CodingCode0
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising NetworksCode0
Large Graph Signal Denoising with Application to Differential PrivacyCode0
Deep Hyperspectral Prior: Denoising, Inpainting, Super-ResolutionCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
Deep Graph Laplacian Regularization for Robust Denoising of Real ImagesCode0
Deep Graph-Convolutional Image DenoisingCode0
Language-Guided Diffusion Model for Visual GroundingCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Learned D-AMP: Principled Neural Network based Compressive Image RecoveryCode0
Learning Instance-Specific Parameters of Black-Box Models Using Differentiable SurrogatesCode0
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval ManuscriptsCode0
Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention FusionCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Multi-scale Processing of Noisy Images using Edge Preservation LossesCode0
Convolutional dictionary learning based auto-encoders for natural exponential-family distributionsCode0
k-Sparse AutoencodersCode0
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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