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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2576–2600 of 7282 papers

TitleStatusHype
Diffusion Model with Representation Alignment for Protein Inverse Folding—0
Analyzing and Improving Model Collapse in Rectified Flow Models—0
Fair Primal Dual Splitting Method for Image Inverse Problems—0
Physics Meets Pixels: PDE Models in Image Processing—0
TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch—0
DALI: Domain Adaptive LiDAR Object Detection via Distribution-level and Instance-level Pseudo Label DenoisingCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
Convergence Analysis of a Proximal Stochastic Denoising Regularization Algorithm—0
Adversarial Contrastive Domain-Generative Learning for Bacteria Raman Spectrum Joint Denoising and Cross-Domain Identification—0
Zero-Shot Mono-to-Binaural Speech Synthesis—0
DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models—0
A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis—0
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-ResolutionCode0
Video Summarization using Denoising Diffusion Probabilistic Model—0
DiffRaman: A Conditional Latent Denoising Diffusion Probabilistic Model for Bacterial Raman Spectroscopy Identification Under Limited Data Conditions—0
Paired Wasserstein Autoencoders for Conditional Sampling—0
Mobile Video Diffusion—0
Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural NetworksCode0
A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing—0
Closed-Form Approximation of the Total Variation Proximal Operator—0
Anomaly detection using Diffusion-based methods—0
Score-Optimal Diffusion Schedules—0
You KAN Do It in a Single Shot: Plug-and-Play Methods with Single-Instance Priors—0
Generative Lines Matching Models—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—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