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 5151–5175 of 7282 papers

TitleStatusHype
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning—0
Self-supervised Physics-based Denoising for Computed Tomography—0
Utilizing the Structure of the Curvelet Transform with Compressed Sensing—0
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising—0
Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions—0
Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging—0
Self-Supervised Siamese Autoencoders—0
Self-Supervised training for blind multi-frame video denoising—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?—0
Self-Tuned Deep Super Resolution—0
Self-Verification in Image Denoising—0
Semantic-Aware Adaptive Video Streaming Using Latent Diffusion Models for Wireless Networks—0
Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization—0
Semantic denoising autoencoders for retinal optical coherence tomography—0
Utilizing the Wavelet Transform's Structure in Compressed Sensing—0
Semantic-Human: Neural Rendering of Humans from Monocular Video with Human Parsing—0
Accelerated first-order primal-dual proximal methods for linearly constrained composite convex programming—0
UTSD: Unified Time Series Diffusion Model—0
Semi-Implicit Functional Gradient Flow for Efficient Sampling—0
Semi-Optimal Edge Detector based on Simple Standard Deviation with Adjusted Thresholding—0
Semi-supervised Learning with Missing Values Imputation—0
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and 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