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 5776–5800 of 7282 papers

TitleStatusHype
Image Processing Tools for Financial Time Series Classification—0
Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness—0
Incorporating Broad Phonetic Information for Speech Enhancement—0
Improved Adaptive Type-2 Fuzzy Filter with Exclusively Two Fuzzy Membership Function for Filtering Salt and Pepper Noise—0
Depth image denoising using nuclear norm and learning graph model—0
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration—0
Exploiting Temporal Attention Features for Effective Denoising in Videos—0
Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning—0
Anti-Bandit Neural Architecture Search for Model Defense—0
Dynamic Low-light Imaging with Quanta Image Sensors—0
A Decoupled Learning Scheme for Real-world Burst Denoising from Raw Images—0
Burst Denoising via Temporally Shifted Wavelet Transforms—0
Unsupervised Sketch to Photo Synthesis—0
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction—0
Reconstructing the Noise Variance Manifold for Image Denoising—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Denoising individual bias for a fairer binary submatrix detectionCode0
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization—0
Unnormalized Variational Bayes—0
Deep learning Framework for Mobile MicroscopyCode0
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising—0
Sparse Nonnegative Tensor Factorization and Completion with Noisy Observations—0
Graph topology inference benchmarks for machine learningCode0
Dynamic Low-light Imaging with Quanta Image Sensors—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