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 2251–2275 of 7282 papers

TitleStatusHype
ECNet: Effective Controllable Text-to-Image Diffusion Models—0
DiffStyler: Diffusion-based Localized Image Style TransferCode1
Global Point Cloud Registration Network for Large TransformationsCode0
DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images—0
Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion ModelCode2
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
Denoising Table-Text Retrieval for Open-Domain Question AnsweringCode0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2—0
Noise2Noise Denoising of CRISM Hyperspectral DataCode1
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation—0
Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models—0
GenesisTex: Adapting Image Denoising Diffusion to Texture Space—0
Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow FieldsCode0
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational AutoencoderCode0
Self-Rectifying Diffusion Sampling with Perturbed-Attention GuidanceCode3
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image ReconstructionCode1
AnimateMe: 4D Facial Expressions via Diffusion Models—0
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation FrameworkCode3
Distilling Semantic Priors from SAM to Efficient Image Restoration Models—0
Multi-Scale Texture Loss for CT denoising with GANsCode0
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation—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