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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 4526–4550 of 7282 papers

TitleStatusHype
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty—0
Iterative Data Programming for Expanding Text Classification Corpora—0
Iterative Denoiser and Noise Estimator for Self-Supervised Image Denoising—0
Iterative execution of discrete and inverse discrete Fourier transforms with applications for signal denoising via sparsification—0
Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models—0
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising—0
Iterative regularization algorithms for image denoising with the TV-Stokes model—0
Iterative Time-Varying Filter Algorithm Based on Discrete Linear Chirp Transform—0
Iterative tomographic reconstruction with TV prior for low-dose CBCT dental imaging—0
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems—0
iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging—0
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives—0
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation—0
Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation—0
JBFnet -- Low Dose CT Denoising by Trainable Joint Bilateral Filtering—0
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution—0
Jeffrey's prior sampling of deep sigmoidal networks—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
Joint Neural AEC and Beamforming with Double-Talk Detection—0
Joint Bilateral Filter for Signal Recovery from Phase Preserved Curvelet Coefficients for Image Denoising—0
Joint Channel, Data, and Radar Parameter Estimation for AFDM Systems in Doubly-Dispersive Channels—0
Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems—0
Joint Demosaicing and Denoising with Perceptual Optimization on a Generative Adversarial Network—0
Joint Demosaicing and Denoising with Double Deep Image Priors—0
The Swiss Army Knife for Image-to-Image Translation: Multi-Task 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