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 3476–3500 of 7282 papers

TitleStatusHype
I Can See Clearly Now : Image Restoration via De-Raining—0
ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications—0
Deep Learning on Image Denoising: An overview—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning—0
Group Sparse Coding for Image Denoising—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—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
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images—0
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction—0
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection—0
Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising—0
Deep Learning for Low-Dose CT Denoising—0
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising—0
Deep Learning for Image Denoising: A Survey—0
Graph Sparsification for Enhanced Conformal Prediction in Graph Neural Networks—0
Graph Signal Restoration Using Nested Deep Algorithm Unrolling—0
Deep learning for full-field ultrasonic characterization—0
Graphs as Tools to Improve Deep Learning Methods—0
Deep Learning for 3D Point Cloud Enhancement: A Survey—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