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Abstract Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips (i.e., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS2and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS2-organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision.
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ADMM approach for efficient iterative tomographic deconvolution reconstruction of 3D quantitative phase images
Tomographic deconvolution phase microscopy (TDPM) is a promising approach for 3D quantitative imaging of phase objects such as biological cells and optical fibers. In the present work, the alternating direction method of multipliers (ADMM) is applied to TDPM to shorten its image acquisition and processing times while simultaneously improving its accuracy. ADMM-TDPM is used to optimize the image fidelity by minimizing Gaussian noise and by using total variation regularization with the constraints of nonnegativity and known zeros. ADMM-TDPM can reconstruct phase objects that are shift variant in three spatial dimensions. ADMM-TDPM achieves speedups of 5x in image acquisition time and greater than 10x in image processing time with accompanying higher accuracy compared to TDPM.
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- Award ID(s):
- 1915971
- PAR ID:
- 10304744
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Applied Optics
- Volume:
- 60
- Issue:
- 27
- ISSN:
- 1559-128X; APOPAI
- Format(s):
- Medium: X Size: Article No. 8485
- Size(s):
- Article No. 8485
- Sponsoring Org:
- National Science Foundation
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