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 5876–5900 of 7282 papers

TitleStatusHype
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
Learning to Rank Intents in Voice Assistants—0
Adversarial Feature Learning and Unsupervised Clustering based Speech Synthesis for Found Data with Acoustic and Textual Noise—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP—0
Identity Enhanced Residual Image DenoisingCode0
Attention Based Real Image RestorationCode0
Deep Photon Mapping—0
Kalman Filter and Wavelet Cross-correlation for VHF Broadband Interferometer Lightning Mapping—0
Accurate Graph Filtering in Wireless Sensor Networks—0
A Review of an Old Dilemma: Demosaicking First, or Denoising First?—0
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix ApproximationCode0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms—0
Frequency-Weighted Robust Tensor Principal Component Analysis—0
The Role of Redundant Bases and Shrinkage Functions in Image Denoising—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
CommUnet: U-net decoder for convolutional codes in communication—0
Superkernel Neural Architecture Search for Image Denoising—0
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging—0
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm—0
Distributed Evolution of Deep Autoencoders—0
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems—0
Self-Supervised training for blind multi-frame video denoising—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—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