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 5801–5825 of 7282 papers

TitleStatusHype
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack—0
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis—0
Spectrum-Guided Adversarial Disparity LearningCode0
Functions with average smoothness: structure, algorithms, and learning—0
Improved Detection of Adversarial Images Using Deep Neural Networks—0
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization—0
JBFnet -- Low Dose CT Denoising by Trainable Joint Bilateral Filtering—0
Learning Model-Blind Temporal Denoisers without Ground Truths—0
Efficient and Parallel Separable Dictionary LearningCode0
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding—0
Consistency analysis of bilevel data-driven learning in inverse problems—0
Robust Technique for Representative Volume Element Identification in Noisy Microtomography Images of Porous Materials Based on Pores Morphology and Their Spatial Distribution—0
Ground Truth Free Denoising by Optimal TransportCode0
Surface Denoising based on Normal Filtering in a Robust Statistics Framework—0
Generating Fluent Translations from Disfluent Text Without Access to Fluent References: IIT Bombay@IWSLT2020—0
Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study—0
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach—0
Hyperspectral Image Denoising with Partially Orthogonal Matrix Vector Tensor Factorization—0
Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments—0
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved GeneralizationCode0
Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis—0
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategyCode0
Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising—0
Block-matching in FPGA—0
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising AutoencodersCode0
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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