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 3621–3630 of 7282 papers

TitleStatusHype
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Benchmarking multi-component signal processing methods in the time-frequency planeCode0
PFCM: Poisson flow consistency models for low-dose CT image denoising—0
Target Score Matching—0
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion modelsCode0
Rolling Diffusion Models—0
Inference Stage Denoising for Undersampled MRI ReconstructionCode0
Computationally efficient reductions between some statistical models—0
An attempt to generate new bridge types from latent space of denoising diffusion Implicit modelCode0
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model—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