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 1901–1925 of 7282 papers

TitleStatusHype
Denoising random forests—0
Denoising Imaging Polarimetry by an Adapted BM3D Method—0
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure—0
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
Blockwise SURE Shrinkage for Non-Local Means—0
Denoising Score Matching with Random Fourier Features—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Denoising Self-attentive Sequential Recommendation—0
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning—0
Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)—0
Denoising single images by feature ensemble revisited—0
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data—0
An Analysis on Quantizing Diffusion Transformers—0
Denoising Task Difficulty-based Curriculum for Training Diffusion Models—0
Blind Biological Sequence Denoising with Self-Supervised Set Learning—0
Denoising Text to Speech with Frame-Level Noise Modeling—0
Denoising Time Cycle Modeling for Recommendation—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
Denoising Hamiltonian Network for Physical Reasoning—0
Denoising guarantees for optimized sampling schemes in compressed sensing—0
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion—0
Denoising via Repainting: an image denoising method using layer wise medical image repainting—0
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation—0
Denoising Weak Lensing Mass Maps with Deep Learning—0
Analysing Diffusion Segmentation for Medical Images—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