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 2326–2350 of 7282 papers

TitleStatusHype
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation—0
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation—0
SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation—0
Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset—0
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy—0
Concentration Inequalities for the Stochastic Optimization of Unbounded Objectives with Application to Denoising Score Matching—0
Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands—0
Learnable Residual-Based Latent Denoising in Semantic Communication—0
SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion—0
A Flag Decomposition for Hierarchical DatasetsCode0
Monte Carlo Tree Diffusion for System 2 Planning—0
FlexControl: Computation-Aware ControlNet with Differentiable Router for Text-to-Image GenerationCode0
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution—0
Do we really have to filter out random noise in pre-training data for language models?—0
Diffusion Models for Computational Neuroimaging: A SurveyCode0
AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution—0
Dual Caption Preference Optimization for Diffusion ModelsCode0
A Comprehensive Survey on Image Signal Processing Approaches for Low-Illumination Image Enhancement—0
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling—0
On Memory Construction and Retrieval for Personalized Conversational Agents—0
Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks—0
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure PurificationCode0
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems—0
Geometric Machine Learning on EEG Signals—0
In-context denoising with one-layer transformers: connections between attention and associative memory retrieval—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