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 6801–6825 of 7282 papers

TitleStatusHype
Efficient and Robust Remote Sensing Image Denoising Using Randomized Approximation of Geodesics' Gramian on the Manifold Underlying the Patch Space—0
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network—0
Efficient and Unbiased Sampling of Boltzmann Distributions via Consistency Models—0
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices—0
Efficient Bearing Sensor Data Compression via an Asymmetrical Autoencoder with a Lifting Wavelet Transform Layer—0
Efficient Blind-Spot Neural Network Architecture for Image Denoising—0
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network—0
Efficient Deep Image Denoising via Class Specific Convolution—0
Efficient Denoising Method to Improve The Resolution of Satellite Images—0
Efficient Denoising using Score Embedding in Score-based Diffusion Models—0
Efficient Dictionary Learning with Sparseness-Enforcing Projections—0
Efficient Diffusion Training through Parallelization with Truncated Karhunen-Loève Expansion—0
Super-resolution Reconstruction of Single Image for Latent features—0
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent—0
Super-Resolving Noisy Images—0
Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images—0
Efficient HDR Reconstruction from Real-World Raw Images—0
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials—0
Efficient joint noise removal and multi exposure fusion—0
Efficient Medical Image Restoration via Reliability Guided Learning in Frequency Domain—0
Efficient Neural Music Generation—0
Efficient Image Denoising by Low-Rank Singular Vector Approximations of Geodesics' Gramian Matrix—0
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators—0
Efficient randomized smoothing by denoising with learned score function—0
Computationally efficient reductions between some statistical models—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