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 7051–7075 of 7282 papers

TitleStatusHype
Fast and High-Quality Image Denoising via Malleable Convolutions—0
Fast and Light-Weight Network for Single Frame Structured Illumination Microscopy Super-Resolution—0
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent—0
Fast and Provable ADMM for Learning with Generative Priors—0
Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms—0
Fast and Robust Visuomotor Riemannian Flow Matching Policy—0
Fast Autoregressive Models for Continuous Latent Generation—0
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
An Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification—0
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery—0
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis—0
Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout—0
A General Framework for Fast Stagewise Algorithms—0
Faster gradient descent and the efficient recovery of images—0
Fast graph-based denoising for point cloud color information—0
FAST-GSC: Fast and Adaptive Semantic Transmission for Generative Semantic Communication—0
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion—0
Synthesis of Through-Wall Micro-Doppler Signatures of Human Motions Using Generative Adversarial Networks—0
Fast Image Deconvolution using Hyper-Laplacian Priors—0
Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes—0
Synthesis versus analysis in patch-based image priors—0
Fast Local Neural Regression for Low-Cost, Path Traced Lambertian Global Illumination—0
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—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