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 6101–6125 of 7282 papers

TitleStatusHype
DEVDAN: Deep Evolving Denoising Autoencoder—0
Noise as Domain Shift: Denoising Medical Images by Unpaired Image TranslationCode0
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—0
Learning Robust Representations with Graph Denoising Policy Network—0
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization—0
Stacked Autoencoder Based Deep Random Vector Functional Link Neural Network for Classification—0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-EncodersCode0
適合漸凍人使用之語音轉換系統初步研究(Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients)—0
CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific Image Inpainting and Image Denoising—0
Self-Guided Network for Fast Image DenoisingCode0
Fast Image Restoration With Multi-Bin Trainable Linear UnitsCode0
Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise—0
Deep K-SVD Denoising—0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Multichannel Speech Enhancement by Raw Waveform-mapping using Fully Convolutional Networks—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS—0
Denoising Improves Latent Space Geometry in Text Autoencoders—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
Samples Are Useful? Not Always: denoising policy gradient updates using variance explained—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
Self-Induced Curriculum Learning in Neural Machine Translation—0
Isolating Latent Structure with Cross-population Variational Autoencoders—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