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 2951–3000 of 7282 papers

TitleStatusHype
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy—0
GPU acceleration of NL-means, BM3D and VBM3D—0
A Topological Loss Function: Image Denoising on a Low-Light Dataset—0
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model—0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion—0
FlexEdit: Flexible and Controllable Diffusion-based Object-centric Image Editing—0
FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios—0
EVRNet: Efficient Video Restoration on Edge Devices—0
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising—0
Convolution by Evolution: Differentiable Pattern Producing Networks—0
Evolvable Conditional Diffusion—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
A fast patch-dictionary method for whole image recovery—0
Evolutionary Variational Optimization of Generative Models—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Convolutional Sparse Coding with Overlapping Group Norms—0
FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models—0
Event-Guided Denoising for Multilingual Relation Learning—0
FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation—0
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems—0
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors—0
Event Guided Denoising for Multilingual Relation Learning—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses—0
FN-Net:Remove the Outliers by Filtering the Noise—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation—0
FocusDiffuser: Perceiving Local Disparities for Camouflaged Object Detection—0
A CT Image Denoising Method with Residual Encoder-Decoder Network—0
FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction—0
Good Similar Patches for Image Denoising—0
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation—0
Event-Customized Image Generation—0
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation—0
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets—0
Foundation Cures Personalization: Recovering Facial Personalized Models' Prompt Consistency—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Convolutional Neural Networks Deceived by Visual Illusions—0
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion—0
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration—0
Fractional Calculus In Image Processing: A Review—0
Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience—0
A Faster Patch Ordering Method for Image Denoising—0
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models—0
Gotta Go Fast with Score-Based Generative Models—0
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models—0
Fréchet regression with implicit denoising and multicollinearity reduction—0
Evaluation of Transfer Learning for Polish with a Text-to-Text Model—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