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 3301–3350 of 7282 papers

TitleStatusHype
Guided Diffusion Model for Sensor Data Obfuscation—0
Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising—0
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models—0
Guided Frequency Loss for Image Restoration—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions—0
Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence—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
Convergence of denoising diffusion models under the manifold hypothesis—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
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal—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
Enhancing Sample Generation of Diffusion Models using Noise Level Correction—0
Enhancing Recommendation with Denoising Auxiliary Task—0
Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—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
HDR Denoising and Deblurring by Learning Spatio-temporal Distortion Models—0
HDR Imaging with Spatially Varying Signal-to-Noise Ratios—0
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction—0
HDRVideo-GAN: Deep Generative HDR Video Reconstruction—0
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models—0
Enhancing Motion Dynamics of Image-to-Video Models via Adaptive Low-Pass Guidance—0
Heart Murmur and Abnormal PCG Detection via Wavelet Scattering Transform & a 1D-CNN—0
Heart Rate Extraction from Abdominal Audio Signals—0
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data—0
Heavy-Tailed Diffusion Models—0
HE-Drive: Human-Like End-to-End Driving with Vision Language Models—0
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model—0
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models—0
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models—0
Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving—0
Heterogeneous quantization regularizes spiking neural network activity—0
Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise—0
Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling—0
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement—0
HiddenSinger: High-Quality Singing Voice Synthesis via Neural Audio Codec and Latent Diffusion Models—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion 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