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 1876–1900 of 7282 papers

TitleStatusHype
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning—0
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection—0
Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback—0
Denoising neural networks for magnetic resonance spectroscopy—0
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network—0
Denoising Induction Motor Sounds Using an Autoencoder—0
Blind Denoising Autoencoder—0
Blob Reconstruction Using Unilateral Second Order Gaussian Kernels With Application to High-ISO Long-Exposure Image Denoising—0
Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations—0
Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction—0
Denoising of image gradients and total generalized variation denoising—0
BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers—0
Denoising Improves Latent Space Geometry in Text Autoencoders—0
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network—0
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator—0
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models—0
Denoising Imaging Polarimetry by an Adapted BM3D Method—0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers—0
Block-matching in FPGA—0
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor—0
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure—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