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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 526–550 of 7282 papers

TitleStatusHype
An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman ProblemCode1
Self-Supervised Diffusion MRI Denoising via Iterative and Stable RefinementCode1
Generative diffusion model with inverse renormalization group flowsCode1
NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator DiffusionCode1
D^2-DPM: Dual Denoising for Quantized Diffusion Probabilistic ModelsCode1
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano TranscriptionCode1
FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI SynthesisCode1
Conditional Consistency Guided Image Translation and EnhancementCode1
T2ICount: Enhancing Cross-modal Understanding for Zero-Shot CountingCode1
Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time AdaptationCode1
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
UHD-processer: Unified UHD Image Restoration with Progressive Frequency Learning and Degradation-aware PromptsCode1
Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image CaptioningCode1
Zero-Shot Blind-spot Image Denoising via Implicit Neural SamplingCode1
KAE: Kolmogorov-Arnold Auto-Encoder for Representation LearningCode1
DDIM sampling for Generative AIBIM, a faster intelligent structural design frameworkCode1
A Conditional Diffusion Model for Electrical Impedance Tomography Image ReconstructionCode1
S^2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph CompletionCode1
Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer AggregationCode1
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy ResponseCode1
Unified Image Restoration and Enhancement: Degradation Calibrated Cycle Reconstruction Diffusion ModelCode1
DiffSim: Taming Diffusion Models for Evaluating Visual SimilarityCode1
Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video DenoisingCode1
3D^2-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar ModelingCode1
Re-Attentional Controllable Video Diffusion EditingCode1
Show:102550
← PrevPage 22 of 292Next →
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