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 5476–5500 of 7282 papers

TitleStatusHype
Saddlepoints in Unsupervised Least Squares—0
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation—0
On tuning consistent annealed sampling for denoising score matching—0
Learning robust speech representation with an articulatory-regularized variational autoencoder—0
Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps—0
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging—0
Hyperspectral Image Denoising Based On Multi-Stream Denoising Network—0
Noise Estimation for Generative Diffusion Models—0
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs—0
Searching Efficient Model-guided Deep Network for Image Denoising—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech—0
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning—0
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network—0
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal ProcessingCode0
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives—0
Advances and Challenges in Unsupervised Neural Machine Translation—0
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Low-dimensional Denoising Embedding Transformer for ECG Classification—0
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool—0
In-Place Scene Labelling and Understanding with Implicit Scene Representation—0
Modeling Graph Node Correlations with Neighbor Mixture Models—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