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 3401–3450 of 7282 papers

TitleStatusHype
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products—0
HORT: Monocular Hand-held Objects Reconstruction with Transformers—0
Handheld Burst Super-Resolution Meets Multi-Exposure Satellite Imagery—0
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery—0
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning—0
Deep Network for Capacitive ECG Denoising—0
Hair and Scalp Disease Detection using Machine Learning and Image Processing—0
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network—0
How to Best Combine Demosaicing and Denoising?—0
How to Construct Energy for Images? Denoising Autoencoder Can Be Energy Based Model—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Deep MMD Gradient Flow without adversarial training—0
How to Unlock Time Series Editing? Diffusion-Driven Approach with Multi-Grained Control—0
How vulnerable is my policy? Adversarial attacks on modern behavior cloning policies—0
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
Adaptive dropout for training deep neural networks—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models—0
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization—0
GUNet: A Graph Convolutional Network United Diffusion Model for Stable and Diversity Pose Generation—0
Guided Speech Enhancement Network—0
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation—0
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure—0
Guided Frequency Loss for Image Restoration—0
Guided Filter based Edge-preserving Image Non-blind Deconvolution—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Backdoor Attacks against Image-to-Image Networks—0
Hume: Introducing System-2 Thinking in Visual-Language-Action Model—0
HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation—0
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations—0
Guided Diffusion Model for Sensor Data Obfuscation—0
Deep Manifold Prior—0
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models—0
Hybrid Digital-Analog Semantic Communications—0
Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers—0
Deeply Coupled Auto-encoder Networks for Cross-view Classification—0
Deeply Cascaded U-Net for Multi-Task Image Processing—0
Guidance Free Image Editing via Explicit Conditioning—0
Deeply Aggregated Alternating Minimization for Image Restoration—0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model—0
Aligning Protein Conformation Ensemble Generation with Physical Feedback—0
Adaptive Domain Learning for Cross-domain Image Denoising—0
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression—0
GSNs : Generative Stochastic Networks—0
Deep Learning with robustness to missing data: A novel approach to the detection of COVID-19—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