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 6826–6850 of 7282 papers

TitleStatusHype
VIGAN: Missing View Imputation with Generative Adversarial NetworksCode0
Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution EnvironmentsCode0
Shapley Value-driven Data Pruning for Recommender SystemsCode0
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving SafetyCode0
Sharpness-aware Low dose CT denoising using conditional generative adversarial networkCode0
Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward AlignmentCode0
Nonlocality-Reinforced Convolutional Neural Networks for Image DenoisingCode0
Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence AutoencoderCode0
How to Segment in 3D Using 2D Models: Automated 3D Segmentation of Prostate Cancer Metastatic Lesions on PET Volumes Using Multi-angle Maximum Intensity Projections and Diffusion ModelsCode0
Data-Adaptive Graph Framelets with Generalized Vanishing Moments for Graph Signal ProcessingCode0
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel EnergiesCode0
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision TransformersCode0
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image EnhancementCode0
Robust and interpretable blind image denoising via bias-free convolutional neural networksCode0
Non-Local Recurrent Network for Image RestorationCode0
HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image RestorationCode0
Dual Caption Preference Optimization for Diffusion ModelsCode0
Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K SamplingCode0
Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoisingCode0
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse SignalsCode0
Non-Local Video Denoising by CNNCode0
An attempt to generate new bridge types from latent space of denoising diffusion Implicit modelCode0
D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised ActivationsCode0
Coupled Dictionary Learning for Multi-contrast MRI ReconstructionCode0
Generative Adversarial Networks for Robust Cryo-EM Image DenoisingCode0
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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