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 3976–4000 of 7282 papers

TitleStatusHype
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models—0
Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model—0
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation—0
Generating Human Interaction Motions in Scenes with Text Control—0
Generating more realistic images using gated MRF's—0
TDCGL: Two-Level Debiased Contrastive Graph Learning for Recommendation—0
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations—0
Generating Synthetic Net Load Data with Physics-informed Diffusion Model—0
Generation of synthetic financial time series by diffusion models—0
Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models—0
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach—0
Generative AI Models for Different Steps in Architectural Design: A Literature Review—0
Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS—0
Generative AI for Physical-Layer Authentication—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks—0
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising—0
Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks—0
Adversarial Signal Denoising with Encoder-Decoder Networks—0
Generative Class-conditional Autoencoders—0
Generative diffusion model surrogates for mechanistic agent-based biological models—0
TDiR: Transformer based Diffusion for Image Restoration Tasks—0
Generative Diffusion Prior for Unified Image Restoration and Enhancement—0
Generative Edge Detection with Stable Diffusion—0
Generative emulation of chaotic dynamics with coherent prior—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