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

Showing 40814090 of 7282 papers

TitleStatusHype
Music2Latent2: Audio Compression with Summary Embeddings and Autoregressive Decoding0
Training-free Diffusion Acceleration with Bottleneck Sampling0
Adaptive Estimation and Learning under Temporal Distribution Shift0
MVDD: Multi-View Depth Diffusion Models0
MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation0
MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation0
Adaptive dropout for training deep neural networks0
N2V2 -- Fixing Noise2Void Checkerboard Artifacts with Modified Sampling Strategies and a Tweaked Network Architecture0
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs0
NAN: Noise-Aware NeRFs for Burst-Denoising0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified