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 4376–4400 of 7282 papers

TitleStatusHype
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising—0
Improved Self-Supervised Deep Image Denoising—0
Improved Sparse Low-Rank Matrix Estimation—0
Improved Stochastic Texture Filtering Through Sample Reuse—0
Improved Techniques for Adversarial Discriminative Domain Adaptation—0
Improving Adversarial Energy-Based Model via Diffusion Process—0
Improving Antibody Design with Force-Guided Sampling in Diffusion Models—0
Improving Audio-Visual Video Parsing with Pseudo Visual Labels—0
A Pseudo-labelling Auto-Encoder for unsupervised image classification—0
The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks—0
Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation—0
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising—0
Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise—0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations—0
The Gaussian Process Latent Autoregressive Model—0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
A Discontinuous Neural Network for Non-Negative Sparse Approximation—0
Improving Diffusion Models for ECG Imputation with an Augmented Template Prior—0
A Directional Diffusion Graph Transformer for Recommendation—0
The Gaussian Transform—0
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models—0
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data—0
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data—0
Improving Interpretation Faithfulness for Vision Transformers—0
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising 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