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 1476–1500 of 7282 papers

TitleStatusHype
Learning an Adaptive Model for Extreme Low-light Raw Image ProcessingCode1
Point Cloud Denoising via Momentum Ascent in Gradient FieldsCode1
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud LearningCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
Learning Deformable Kernels for Image and Video DenoisingCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image RestorationCode1
Learning from Rules Generalizing Labeled ExemplarsCode1
Learn from Unpaired Data for Image Restoration: A Variational Bayes ApproachCode1
Practical Deep Raw Image Denoising on Mobile DevicesCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Deep Image PriorCode1
Learning A Coarse-to-Fine Diffusion Transformer for Image RestorationCode1
Data-Driven Denoising of Stationary Accelerometer SignalsCode1
Pre-training via Denoising for Molecular Property PredictionCode1
A Cheaper and Better Diffusion Language Model with Soft-Masked NoiseCode1
Privacy-Preserving Diffusion Model Using Homomorphic EncryptionCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
Learning a Diffusion Model Policy from Rewards via Q-Score MatchingCode1
Learning Graph-Convolutional Representations for Point Cloud DenoisingCode1
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
Deep Equilibrium Approaches to Diffusion ModelsCode1
Deep Energy Estimator NetworksCode1
Certified Robustness for Large Language Models with Self-DenoisingCode1
LatentEditor: Text Driven Local Editing of 3D ScenesCode1
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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