Learning Image Formation and Regularization in Unrolling AMP for Lensless Image Reconstruction

This paper proposes an unrolling learnable approximate message passing recurrent neural network (called ULAMP-Net) for lensless image reconstruction. By unrolling the optimization iterations, key modules and parameters are made learnable to achieve high reconstruction quality. Specifically, observat...

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Veröffentlicht in:	IEEE transactions on computational imaging Jg. 8; S. 479 - 489
Hauptverfasser:	Yang, Jingyu, Yin, Xiangjun, Zhang, Mengxi, Yue, Huihui, Cui, Xingyu, Yue, Huanjing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	approximate message passing algorithm Cameras Current measurement deep unfolding method Image reconstruction Imaging Learning Lensless imaging Measurement uncertainty Message passing Modules Multilayer perceptrons Multilayers Optimization Parameters Point spread functions Prototypes Recurrent neural networks Regularization spatial-channel attention Systematic errors Training
ISSN:	2573-0436, 2333-9403
Online-Zugang:	Volltext
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