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 1891–1900 of 7282 papers

TitleStatusHype
Surgical Triplet Recognition via Diffusion Model—0
RobGC: Towards Robust Graph Condensation—0
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized RationalesCode2
Neural Residual Diffusion Models for Deep Scalable Vision GenerationCode0
MaskPure: Improving Defense Against Text Adversaries with Stochastic PurificationCode0
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User FeedbackCode4
Training Diffusion Models with Federated Learning—0
Immiscible Diffusion: Accelerating Diffusion Training with Noise AssignmentCode2
Variational Distillation of Diffusion Policies into Mixture of Experts—0
Evaluating the design space of diffusion-based generative models—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