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 4301–4325 of 7282 papers

TitleStatusHype
Image Denoising using Attention-Residual Convolutional Neural Networks—0
Image Denoising Using Convolutional Autoencoder—0
Diffusion prior as a direct regularization term for FWI—0
Image denoising using group sparsity residual and external nonlocal self-similarity prior—0
Image Denoising Using Low Rank Minimization With Modified Noise Estimation—0
Image Denoising using New Adaptive Based Median Filters—0
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach—0
Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization—0
Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples—0
Image Denoising via CNNs: An Adversarial Approach—0
Image Denoising via Collaborative Dual-Domain Patch Filtering—0
Image Denoising Via Collaborative Support-Agnostic Recovery—0
Image denoising via group sparsity residual constraint—0
Image denoising via K-SVD with primal-dual active set algorithm—0
Image Denoising via Multi-scale Nonlinear Diffusion Models—0
Image Denoising via Style Disentanglement—0
Image denoising with generalized Gaussian mixture model patch priors—0
Image Denoising with Kernels based on Natural Image Relations—0
Image denosing in underwater acoustic noise using discrete wavelet transform with different noise level estimation—0
Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving—0
Image Deraining via Self-supervised Reinforcement Learning—0
Image Edge Restoring Filter—0
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm—0
Image Generation from Contextually-Contradictory Prompts—0
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—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