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 7151–7175 of 7282 papers

TitleStatusHype
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
A Diffusion Model for Event Skeleton GenerationCode0
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal DenoisingCode0
Efficient Bi-Level Optimization for Recommendation DenoisingCode0
MicroSSIM: Improved Structural Similarity for Comparing Microscopy DataCode0
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoisingCode0
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 ClassificationCode0
Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity BiasCode0
A Neural Network for SemigroupsCode0
Anatomical Priors for Image Segmentation via Post-Processing with Denoising AutoencodersCode0
Specify and Edit: Overcoming Ambiguity in Text-Based Image EditingCode0
Incomplete Gamma Kernels: Generalizing Locally Optimal Projection OperatorsCode0
Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising DiffusionCode0
Exploiting Social Media Content for Self-Supervised Style TransferCode0
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategyCode0
Unsupervised Question Answering via Answer DiversifyingCode0
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel DataCode0
Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural NetworksCode0
SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion ModelsCode0
IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target RecognitionCode0
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style TransferCode0
Decomposition of Higher-Order Spectra for Blind Multiple-Input Deconvolution, Pattern Identification and SeparationCode0
Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category PreferencesCode0
Radio Map Estimation via Latent Domain Plug-and-Play DenoisingCode0
Index NetworkCode0
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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