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 5951–5975 of 7282 papers

TitleStatusHype
Multiple Imputation with Denoising Autoencoder using Metamorphic Truth and Imputation Feedback—0
SD-GAN: Structural and Denoising GAN reveals facial parts under occlusion—0
Multi-wavelet residual dense convolutional neural network for image denoising—0
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks—0
NoiseBreaker: Gradual Image Denoising Guided by Noise Analysis—0
Tensor denoising and completion based on ordinal observations—0
Efficient graph construction for image representationCode0
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
Boosted Locality Sensitive Hashing: Discriminative Binary Codes for Source SeparationCode0
Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation—0
Utilizing the Wavelet Transform's Structure in Compressed Sensing—0
3D Point Cloud Enhancement using Graph-Modelled Multiview Depth Measurements—0
Reconstructing the Noise Manifold for Image Denoising—0
Free-breathing Cardiovascular MRI Using a Plug-and-Play Method with Learned Denoiser—0
GPU acceleration of NL-means, BM3D and VBM3D—0
Translating Diffusion, Wavelets, and Regularisation into Residual Networks—0
Differentiable Forward and Backward Fixed-Point Iteration Layers—0
SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning—0
Iterative Data Programming for Expanding Text Classification Corpora—0
Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder—0
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling—0
An optimized pipeline for functional connectivity analysis in the rat brainCode0
Handling noise in image deblurring via joint learning—0
Proximal Gradient Algorithms: Applications in Signal Processing—0
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks—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