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 1801–1825 of 7282 papers

TitleStatusHype
ERD: Exponential Retinex decomposition based on weak space and hybrid nonconvex regularization and its denoising application—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models—0
What's the score? Automated Denoising Score Matching for Nonlinear Diffusions—0
Systematic Evaluation of Neural Retrieval Models on the Touché 2020 Argument Retrieval Subset of BEIRCode0
Coherent and Multi-modality Image Inpainting via Latent Space OptimizationCode2
Multi-modal Crowd Counting via a Broker ModalityCode1
DuInNet: Dual-Modality Feature Interaction for Point Cloud Completion—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
Asymmetric Mask Scheme for Self-Supervised Real Image DenoisingCode1
Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative ModelsCode0
Implicit Regression in Subspace for High-Sensitivity CEST Imaging—0
Diffusion Models: Tutorial and Survey—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—0
Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total VariationCode1
Heterogeneous window transformer for image denoisingCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Minutes to Seconds: Speeded-up DDPM-based Image Inpainting with Coarse-to-Fine SamplingCode2
Structured Generations: Using Hierarchical Clusters to guide Diffusion ModelsCode0
LaSe-E2V: Towards Language-guided Semantic-Aware Event-to-Video Reconstruction—0
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion—0
Image-Conditional Diffusion Transformer for Underwater Image Enhancement—0
FreeCompose: Generic Zero-Shot Image Composition with Diffusion PriorCode1
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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