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 5501–5525 of 7282 papers

TitleStatusHype
AIM 2020 Challenge on Learned Image Signal Processing PipelineCode1
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data—0
Denoising Relation Extraction from Document-level Distant SupervisionCode1
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
Suppression of Correlated Noise with Similarity-based Unsupervised Deep LearningCode1
A Comprehensive Comparison of Multi-Dimensional Image Denoising MethodsCode0
Noise Reduction to Compute Tissue Mineral Density and Trabecular Bone Volume Fraction from Low Resolution QCT—0
Convolutional Proximal Neural Networks and Plug-and-Play AlgorithmsCode1
Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective—0
A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System—0
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations—0
BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image RestorationCode1
Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfittingCode1
Patch2Self: Denoising Diffusion MRI with Self-Supervised LearningCode1
Revisiting Adaptive Convolutions for Video Frame Interpolation—0
Deep Pairwise Hashing for Cold-start RecommendationCode0
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation—0
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT DenoisingCode1
Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising—0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRICode0
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection—0
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image RestorationCode1
Autoregressive Score Matching—0
Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network—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