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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 3226–3250 of 7282 papers

TitleStatusHype
Gotta Go Fast with Score-Based Generative Models—0
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors—0
EP-CFG: Energy-Preserving Classifier-Free Guidance—0
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals—0
GPU acceleration of NL-means, BM3D and VBM3D—0
多樣訊雜比之訓練語料於降噪自動編碼器其語音強化功能之初步研究 (A Preliminary Study of Various SNR-level Training Data in the Denoising Auto-encoder (DAE) Technique for Speech Enhancement) [In Chinese]—0
GradCheck: Analyzing classifier guidance gradients for conditional diffusion sampling—0
AdvLogo: Adversarial Patch Attack against Object Detectors based on Diffusion Models—0
Gradient-based Point Cloud Denoising with Uniformity—0
Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank—0
Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion—0
Entrywise Inference for Missing Panel Data: A Simple and Instance-Optimal Approach—0
Gradient Domain Weighted Guided Image Filtering—0
Entropy stable conservative flux form neural networks—0
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Gradual Training Method for Denoising Auto Encoders—0
Gradual training of deep denoising auto encoders—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Convergence of the denoising diffusion probabilistic models for general noise schedules—0
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects—0
Graph-based denoising for time-varying point clouds—0
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms—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