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
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising—0
Momentum-Net for Low-Dose CT Image Reconstruction—0
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling—0
Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance NormalizationCode0
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation—0
Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude EstimationCode1
Blind Channel Estimation for Massive MIMO: A Deep Learning Assisted Approach—0
Generalized Octave Convolutions for Learned Multi-Frequency Image CompressionCode0
The Maximum Entropy on the Mean Method for Image Deblurring—0
Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN—0
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging ProblemsCode1
Self-Supervised Poisson-Gaussian DenoisingCode1
SD-GAN: Structural and Denoising GAN reveals facial parts under occlusion—0
Multi-wavelet residual dense convolutional neural network for image denoising—0
Multiple Imputation with Denoising Autoencoder using Metamorphic Truth and Imputation Feedback—0
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks—0
NoiseBreaker: Gradual Image Denoising Guided by Noise Analysis—0
Efficient graph construction for image representationCode0
Tensor denoising and completion based on ordinal observations—0
SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression—0
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation—0
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source SeparationCode0
Replacing Mobile Camera ISP with a Single Deep Learning ModelCode1
3D Point Cloud Enhancement using Graph-Modelled Multiview Depth Measurements—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