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 6876–6900 of 7282 papers

TitleStatusHype
Surface Denoising based on Normal Filtering in a Robust Statistics Framework—0
Energy-Based Processes for Exchangeable Data—0
A generative foundation model for an all-in-one seismic processing framework—0
Energy Dissipation with Plug-and-Play Priors—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Enhanced 3D Generation by 2D Editing—0
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing—0
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus—0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
Enhanced CNN for image denoising—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling—0
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net—0
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation—0
Enhanced Low-Rank Matrix Approximation—0
Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models—0
Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis—0
Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders—0
Enhancement of the Prefiltered Rotationally Invariant Non-local PCA Algorithm for MRI—0
Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction—0
Surgical Triplet Recognition via Diffusion Model—0
Enhancing Crowdsourced Audio for Text-to-Speech Models—0
Enhancing CTR Prediction in Recommendation Domain with Search Query Representation—0
Enhancing Diffusion Models for High-Quality Image Generation—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