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 4301–4350 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
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions—0
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal FusionCode0
Least Squares Regression Can Exhibit Under-Parameterized Double Descent—0
Textless Speech-to-Speech Translation With Limited Parallel DataCode0
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation—0
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models—0
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking—0
Realistic Noise Synthesis with Diffusion ModelsCode0
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale—0
A Dive into SAM Prior in Image Restoration—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing—0
Why current rain denoising models fail on CycleGAN created rain images in autonomous driving—0
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer—0
Extrapolating Multilingual Understanding Models as Multilingual Generators—0
DCCRN-KWS: an audio bias based model for noise robust small-footprint keyword spotting—0
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model—0
Guided Motion Diffusion for Controllable Human Motion Synthesis—0
Moment Matching Denoising Gibbs SamplingCode0
A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy—0
The probability flow ODE is provably fast—0
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing—0
SIDAR: Synthetic Image Dataset for Alignment & RestorationCode0
NODE-ImgNet: a PDE-informed effective and robust model for image denoisingCode0
Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis—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