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 1076–1100 of 7282 papers

TitleStatusHype
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion ModelsCode1
Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric VideosCode1
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
AdsorbDiff: Adsorbate Placement via Conditional Denoising DiffusionCode1
DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion ModelCode1
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic ModelsCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
DiffAR: Denoising Diffusion Autoregressive Model for Raw Speech Waveform GenerationCode1
Coarse-To-Fine Tensor Trains for Compact Visual RepresentationsCode1
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
Differentiable Manifold Reconstruction for Point Cloud DenoisingCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Diffusion Probabilistic Modeling for Video GenerationCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
A Comparison of Image Denoising MethodsCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
Comparison of Image Quality Models for Optimization of Image Processing SystemsCode1
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image SynthesisCode1
AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic ModelsCode1
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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