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 5751–5775 of 7282 papers

TitleStatusHype
Composing Parts for Expressive Object Generation—0
Comprehensive Study on Denoising of Medical Images Utilizing Neural Network Based Auto-Encoder—0
An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio—0
Solving Inverse Problems with a Flow-based Noise Model—0
Compressed Sensing with Invertible Generative Models and Dependent Noise—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
An approach to image denoising using manifold approximation without clean images—0
Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update—0
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth—0
Compression of user generated content using denoised references—0
Compression-Realized Deep Structural Network for Video Quality Enhancement—0
Compressive Sensing with Tensorized Autoencoder—0
Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data—0
Computational Intractability of Dictionary Learning for Sparse Representation—0
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review—0
Computationally iterative methods for salt-and-pepper denoising—0
Variational Distillation of Diffusion Policies into Mixture of Experts—0
Concatenated Attention Neural Network for Image Restoration—0
Concealed Object Detection for Passive Millimeter-Wave Security Imaging Based on Task-Aligned Detection Transformer—0
Concentration Inequalities for the Stochastic Optimization of Unbounded Objectives with Application to Denoising Score Matching—0
Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation—0
Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data—0
ConDiSim: Conditional Diffusion Models for Simulation Based Inference—0
Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation—0
Dynamic Attention-Guided Diffusion for Image Super-Resolution—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