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 4301–4325 of 7282 papers

TitleStatusHype
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation—0
SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds—0
Addressing Negative Transfer in Diffusion Models—0
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models—0
Improving Handwritten OCR with Training Samples Generated by Glyph Conditional Denoising Diffusion Probabilistic Model—0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles—0
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection—0
Infrared Image Deturbulence Restoration Using Degradation Parameter-Assisted Wide & Deep LearningCode0
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold RobustnessCode0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular DynamicsCode0
On the Noise Sensitivity of the Randomized SVDCode0
A Diffusion Model for Event Skeleton GenerationCode0
Error Bounds for Flow Matching Methods—0
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers—0
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent—0
Zero-shot Generation of Training Data with Denoising Diffusion Probabilistic Model for Handwritten Chinese Character Recognition—0
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition—0
Efficient Neural Music Generation—0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
SAIL: Search-Augmented Instruction 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