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 3301–3325 of 7282 papers

TitleStatusHype
Guided Diffusion Model for Sensor Data Obfuscation—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Convergence of denoising diffusion models under the manifold hypothesis—0
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models—0
Deep MMD Gradient Flow without adversarial training—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
Enhancing Recommendation with Denoising Auxiliary Task—0
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training—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
Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm—0
A salt and pepper noise image denoising method based on the generative classification—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—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