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 4326–4350 of 7282 papers

TitleStatusHype
TVCondNet: A Conditional Denoising Neural Network for NMR Spectroscopy—0
On the exact relationship between the denoising function and the data distribution—0
On the Feature Learning in Diffusion Models—0
Adapting MIMO video restoration networks to low latency constraints—0
On the impact of incorporating task-information in learning-based image denoising—0
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models—0
On the interplay of network structure and gradient convergence in deep learning—0
On the Interpolation Effect of Score Smoothing—0
On the Kullback-Leibler divergence between pairwise isotropic Gaussian-Markov random fields—0
On the Limitations of Denoising Strategies as Adversarial Defenses—0
On the locality bias and results in the Long Range Arena—0
On the nonparametric maximum likelihood estimator for Gaussian location mixture densities with application to Gaussian denoising—0
On the Optimal Solution of Weighted Nuclear Norm Minimization—0
On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring—0
On the phase diagram of extensive-rank symmetric matrix denoising beyond rotational invariance—0
Two-Dimensional Unknown View Tomography from Unknown Angle Distributions—0
On the Relation between Color Image Denoising and Classification—0
On the Relation Between Linear Diffusion and Power Iteration—0
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders—0
Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement—0
On the Scalability of Diffusion-based Text-to-Image Generation—0
On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models—0
On the Taut String Interpretation of the One-dimensional Rudin-Osher-Fatemi Model: A New Proof, a Fundamental Estimate and Some Applications—0
On the Transformation of Latent Space in Autoencoders—0
On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion 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