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 3451–3475 of 7282 papers

TitleStatusHype
Guidance Free Image Editing via Explicit Conditioning—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Adaptive Domain Learning for Cross-domain Image Denoising—0
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression—0
GSNs : Generative Stochastic Networks—0
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization—0
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—0
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature—0
GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction—0
Group Sparsity Residual with Non-Local Samples for Image Denoising—0
Group Sparsity Residual Constraint for Image Denoising—0
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization—0
Deep Learning on Image Denoising: An overview—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
Hyperspectral Image Recovery via Hybrid Regularization—0
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm—0
Group Sparse Coding for Image Denoising—0
Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos—0
Deep-Learning Inversion Method for the Interpretation of Noisy Logging-While-Drilling Resistivity Measurements—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Grayscale to Hyperspectral at Any Resolution Using a Phase-Only Lens—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
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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