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 826850 of 7282 papers

TitleStatusHype
A zero-inflated gamma model for deconvolved calcium imaging tracesCode1
Artifact Restoration in Histology Images with Diffusion Probabilistic ModelsCode1
Controlling Latent Diffusion Using Latent CLIPCode1
Backpropagation-Friendly EigendecompositionCode1
Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time StepsCode1
Entity-Level Text-Guided Image ManipulationCode1
4DenoiseNet: Adverse Weather Denoising from Adjacent Point CloudsCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
DenoMamba: A fused state-space model for low-dose CT denoisingCode1
3D Brain and Heart Volume Generative Models: A SurveyCode1
Enhancing Creative Generation on Stable Diffusion-based ModelsCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data HidingCode1
EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound NoiseCode1
Enhancing MMDiT-Based Text-to-Image Models for Similar Subject GenerationCode1
Entropy-driven Sampling and Training Scheme for Conditional Diffusion GenerationCode1
Homotopic Gradients of Generative Density Priors for MR Image ReconstructionCode1
Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time AdaptationCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion ModelsCode1
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
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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