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 4776–4800 of 7282 papers

TitleStatusHype
LW-ISP: A Lightweight Model with ISP and Deep Learning—0
Novel View Synthesis with Diffusion Models—0
Progressive Text-to-Image Generation—0
Polar Encoding: A Simple Baseline Approach for Classification with Missing Values—0
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUCode0
EraseNet: A Recurrent Residual Network for Supervised Document Cleaning—0
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration—0
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising—0
Data Synthesis and Iterative Refinement for Neural Semantic Parsing without Annotated Logical Forms—0
Diagnostic Quality Assessment for Low-Dimensional ECG Representations—0
Automatic Summarization for Creative Writing: BART based Pipeline Method for Generating Summary of Movie Scripts—0
Deep Perceptual Enhancement for Medical Image AnalysisCode0
Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching—0
Towards Making the Most of Pre-trained Translation Model for Quality Estimation—0
CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation ExtractionCode0
Programmable Annotation with Diversed Heuristics and Data Denoising—0
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation—0
Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20 Speedup—0
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis—0
Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions—0
Bidirectional Language Models Are Also Few-shot Learners—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 ClassificationCode0
Deep Sparse and Low-Rank Prior for Hyperspectral Image DenoisingCode0
Speech Enhancement Using Self-Supervised Pre-Trained Model and Vector Quantization—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