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 551–575 of 7282 papers

TitleStatusHype
Adaptive Graph Contrastive Learning for RecommendationCode1
ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow PredictionCode1
Diff-Reg v1: Diffusion Matching Model for Registration ProblemCode1
DiffQRCoder: Diffusion-based Aesthetic QR Code Generation with Scanning Robustness Guided Iterative RefinementCode1
DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial TrainingCode1
DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintsCode1
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain PackingCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging LoadCode1
Diffusion Model for Dense MatchingCode1
Empowering Diffusion Models on the Embedding Space for Text GenerationCode1
DiffPO: A causal diffusion model for learning distributions of potential outcomesCode1
Are Deep Neural Architectures Losing Information? Invertibility Is IndispensableCode1
3D Brain and Heart Volume Generative Models: A SurveyCode1
Adaptive Dynamic Filtering Network for Image DenoisingCode1
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising ModelsCode1
DiffPortrait3D: Controllable Diffusion for Zero-Shot Portrait View SynthesisCode1
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic ModelCode1
Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time StepsCode1
Differentiable Manifold Reconstruction for Point Cloud DenoisingCode1
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion ModelsCode1
Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEGCode1
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual PerceptionCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
DiffCMR: Fast Cardiac MRI Reconstruction with 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