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 701–725 of 7282 papers

TitleStatusHype
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward GuidanceCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
DR2: Diffusion-based Robust Degradation Remover for Blind Face RestorationCode1
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion ModelCode1
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based PenaltiesCode1
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack SegmentationCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
Curriculum Disentangled Recommendation with Noisy Multi-feedbackCode1
Dual-Scale Transformer for Large-Scale Single-Pixel ImagingCode1
DPM-OT: A New Diffusion Probabilistic Model Based on Optimal TransportCode1
Customizing 360-Degree Panoramas through Text-to-Image Diffusion ModelsCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
Controlling Latent Diffusion Using Latent CLIPCode1
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup FunctionalCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and GeneralizationCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
Don't Play Favorites: Minority Guidance for Diffusion ModelsCode1
Asymmetric Mask Scheme for Self-Supervised Real Image DenoisingCode1
A generic diffusion-based approach for 3D human pose prediction in the wildCode1
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising DiffusionCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
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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