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 4126–4150 of 7282 papers

TitleStatusHype
Guided Diffusion Model for Sensor Data Obfuscation—0
Tensor Denoising via Amplification and Stable Rank Methods—0
Guided Filter based Edge-preserving Image Non-blind Deconvolution—0
Guided Frequency Loss for Image Restoration—0
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis—0
Guided Speech Enhancement Network—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—0
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction—0
Hair and Scalp Disease Detection using Machine Learning and Image Processing—0
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning—0
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Handling Background Noise in Neural Speech Generation—0
Handling noise in image deblurring via joint learning—0
Handling Noise in Single Image Deblurring Using Directional Filters—0
HanDrawer: Leveraging Spatial Information to Render Realistic Hands Using a Conditional Diffusion Model in Single Stage—0
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN—0
Low-Rank Tensor Completion Based on Bivariate Equivalent Minimax-Concave Penalty—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
HC^3L-Diff: Hybrid conditional latent diffusion with high frequency enhancement for CBCT-to-CT synthesis—0
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization—0
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion 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