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

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Showing 2676–2700 of 7282 papers

TitleStatusHype
Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT—0
Cross-Domain Image Conversion by CycleDM—0
Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection—0
Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation—0
First order algorithms in variational image processing—0
Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response—0
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks—0
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement—0
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift—0
Cross-conditioned Diffusion Model for Medical Image to Image Translation—0
A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image—0
FINO: Flow-based Joint Image and Noise Model—0
Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets—0
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Critically Damped Third-Order Langevin Dynamics—0
Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs—0
Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches—0
AAMDM: Accelerated Auto-regressive Motion Diffusion Model—0
FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model—0
Increasing Iterate Averaging for Solving Saddle-Point Problems—0
CRISPnet: Color Rendition ISP Net—0
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation—0
A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants—0
Creatively Upscaling Images with Global-Regional Priors—0
Crafting Parts for Expressive Object Composition—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