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

Showing 11011125 of 7282 papers

TitleStatusHype
Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based ApproachCode1
Diffusion Action SegmentationCode1
Masked Autoencoders as Image ProcessorsCode1
Real-time Controllable Denoising for Image and VideoCode1
WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion ModelsCode1
4D Facial Expression Diffusion ModelCode1
Spatially Adaptive Self-Supervised Learning for Real-World Image DenoisingCode1
DiffTAD: Temporal Action Detection with Proposal Denoising DiffusionCode1
Seer: Language Instructed Video Prediction with Latent Diffusion ModelsCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up TablesCode1
End-to-End Diffusion Latent Optimization Improves Classifier GuidanceCode1
Pix2Video: Video Editing using Image DiffusionCode1
E-MLB: Multilevel Benchmark for Event-Based Camera DenoisingCode1
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image RestorationCode1
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion ModelsCode1
Localizing Object-level Shape Variations with Text-to-Image Diffusion ModelsCode1
Diff-UNet: A Diffusion Embedded Network for Volumetric SegmentationCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Adversarial Counterfactual Visual ExplanationsCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
P+: Extended Textual Conditioning in Text-to-Image GenerationCode1
DS-Fusion: Artistic Typography via Discriminated and Stylized DiffusionCode1
Efficient Diffusion Training via Min-SNR Weighting StrategyCode1
Hybrid Spectral Denoising Transformer with Guided AttentionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified