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 5526–5550 of 7282 papers

TitleStatusHype
BM3D vs 2-Layer ONN—0
Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image DenoisingCode0
DeepFN: Towards Generalizable Facial Action Unit Recognition with Deep Face Normalization—0
Feature-Align Network with Knowledge Distillation for Efficient Denoising—0
Solving Inverse Problems by Joint Posterior Maximization with Autoencoding PriorCode0
Efficient Deep Image Denoising via Class Specific Convolution—0
Deep Learning strategies for ProtoDUNE raw data denoisingCode0
Image denoising using complex-valued deep CNNCode0
Unsupervised dynamic modeling of medical image transformationCode0
Should EBMs model the energy or the score?—0
Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue—0
Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting—0
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors—0
Handling Background Noise in Neural Speech Generation—0
EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks—0
Spatial-temporal switching estimators for imaging locally concentrated dynamics—0
Interpretable Stability Bounds for Spectral Graph Filters—0
NFCNN: Toward a Noise Fusion Convolutional Neural Network for Image Denoising—0
Training Stacked Denoising Autoencoders for Representation Learning—0
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder—0
Context-Aware Prosody Correction for Text-Based Speech Editing—0
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality—0
Learning from Natural Noise to Denoise Micro-Doppler Spectrogram—0
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders—0
Multimodal Data Visualization and Denoising with Integrated Diffusion—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