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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5701–5725 of 7282 papers

TitleStatusHype
SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategyCode0
Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis—0
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising—0
SAR2SAR: a semi-supervised despeckling algorithm for SAR imagesCode1
Block-matching in FPGA—0
Flexible Image Denoising with Multi-layer Conditional Feature ModulationCode1
Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image RestorationCode1
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising AutoencodersCode0
Inexact Derivative-Free Optimization for Bilevel LearningCode0
The Gaussian Transform—0
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network—0
Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison (Extended Cut)—0
Denoising Diffusion Probabilistic ModelsCode2
Concatenated Attention Neural Network for Image Restoration—0
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift—0
Unified Representation Learning for Efficient Medical Image Analysis—0
Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling—0
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors—0
Image Deconvolution via Noise-Tolerant Self-Supervised InversionCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Gaussian Gated Linear NetworksCode0
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial NetworksCode1
Supervised Learning of Sparsity-Promoting Regularizers for Denoising—0
Probabilistic AutoencoderCode1
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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