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 26512675 of 7282 papers

TitleStatusHype
Generating Training Data for Denoising Real RGB Images via Camera Pipeline SimulationCode0
Generating symbolic music using diffusion modelsCode0
Anomaly Detection with Robust Deep AutoencodersCode0
Microscopy Image Restoration with Deep Wiener-Kolmogorov filtersCode0
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-DecodingCode0
Generative Flows as a General Purpose Solution for Inverse ProblemsCode0
Adaptive Multi-step Refinement Network for Robust Point Cloud RegistrationCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
Generalized Robust Fundus Photography-based Vision Loss Estimation for High MyopiaCode0
A Diffusion Model for Event Skeleton GenerationCode0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
Generalized Denoising Auto-Encoders as Generative ModelsCode0
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT DenoisingCode0
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer GenerationCode0
Anomaly Detection and Prototype Selection Using Polyhedron CurvatureCode0
Generalized Deep Image to Image RegressionCode0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic modelCode0
Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of ConceptCode0
Diffusion Models with Deterministic Normalizing Flow PriorsCode0
Diffusion models under low-noise regimeCode0
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and DecodingCode0
Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-based ApproachCode0
Gaussian Gated Linear NetworksCode0
Classifier-Free Guidance inside the Attraction Basin May Cause MemorizationCode0
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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