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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 651–675 of 7282 papers

TitleStatusHype
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive TrainingCode1
FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral ImagesCode1
Multi-modal Crowd Counting via a Broker ModalityCode1
Asymmetric Mask Scheme for Self-Supervised Real Image DenoisingCode1
Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total VariationCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Heterogeneous window transformer for image denoisingCode1
FreeCompose: Generic Zero-Shot Image Composition with Diffusion PriorCode1
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image SynthesisCode1
ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic ForecastingCode1
Stochastic Solutions for Simultaneous Seismic Data Denoising and Reconstruction via Score-based Generative ModelsCode1
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image ClarityCode1
TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizingCode1
Zero-Shot Image Denoising for High-Resolution Electron MicroscopyCode1
Consistency^2: Consistent and Fast 3D Painting with Latent Consistency ModelsCode1
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
DenoiseRep: Denoising Model for Representation LearningCode1
COVE: Unleashing the Diffusion Feature Correspondence for Consistent Video EditingCode1
Graph Bottlenecked Social RecommendationCode1
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionsCode1
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse ProblemsCode1
Stable Neighbor Denoising for Source-free Domain Adaptive SegmentationCode1
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel NoisesCode1
Denoising-Aware Contrastive Learning for Noisy Time SeriesCode1
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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