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 6026–6050 of 7282 papers

TitleStatusHype
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
Decrypting the temperature field in flow boiling with latent diffusion models—0
Spatially Variant Linear Representation Models for Joint Filtering—0
Spatial-Spectral Diffusion Contrastive Representation Network for Hyperspectral Image Classification—0
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery—0
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—0
Deep Belief Networks for Image Denoising—0
Deep Boosting for Image Denoising—0
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders—0
Analog Image Denoising with an Adaptive Memristive Crossbar Network—0
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing—0
Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation—0
Deep Convolutional Framelet Denoising for Panoramic by Mixed Wavelet Integration—0
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
Deep Denoising Auto-encoder for Statistical Speech Synthesis—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Spatial-temporal switching estimators for imaging locally concentrated dynamics—0
Deep Denoising Method for Side Scan Sonar Images without High-quality Reference Data—0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments—0
Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar—0
Rate-Optimal Denoising with Deep Neural Networks—0
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior—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