.png){kind=icon}
Return
Multi-input mutual supervision network for single-pixel computational imaging
.png){kind=icon}
OA
AI
DOI：10.1364/OE.510683.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this study, we propose a single -pixel computational imaging method based on a multi -input mutual supervision network (MIMSN). We input one-dimensional (1D) light intensity signals and two-dimensional (2D) random image signal into MIMSN, enabling the network to learn the correlation between the two signals and achieve information complementarity. The 2D signal provides spatial information to the reconstruction process, reducing the uncertainty of the reconstructed image. The mutual supervision of the reconstruction results for these two signals brings the reconstruction objective closer to the ground truth image. The 2D images generated by the MIMSN can be used as inputs for subsequent iterations, continuously merging prior information to ensure high -quality imaging at low sampling rates. The reconstruction network does not require pretraining, and 1D signals collected by a single -pixel detector serve as labels for the network, enabling high -quality image reconstruction in unfamiliar environments. Especially in scattering environments, it holds significant potential for applications.
Journal
IF:
3.3
Papers:
6.1W
Citations:
14.3W
Organization
Cited Papers
Deep learning massively accelerates super-resolution localization microscopy.png)
NATURE BIOTECHNOLOGY
OAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveA review of uncertainty quantification in deep learning: Techniques, applications and challengesINFORMATION FUSIONOAAI
INFORMATION FUSIONIF15.5
OAAIShareSaveZinc-oleate complex as efficient precursor for 1-D ZnO nanostructures: synthesis and propertiesCrystEngComm
CrystEngCommIF0
PREAI
ShareSaveShareSaveShareSave
View more