CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration

TL;DR

CalibFPA introduces an online deep-learning calibration method for compressive focal plane array imaging, eliminating the need for offline calibration and improving high-resolution image reconstruction from multiplexed low-resolution measurements.

Contribution

The paper presents a novel online calibration approach using deep learning for compressive FPA systems, addressing limitations of traditional offline calibration methods.

Findings

Outperforms state-of-the-art compressive FPA methods on simulated and experimental data.

Effectively corrects system non-idealities without offline calibration scans.

Demonstrates improved image reconstruction quality and computational efficiency.

Abstract

Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiplexed encoding of the visual scene is typically performed via electronically controllable spatial light modulators (SLM). An HR image is then reconstructed from the encoded measurements by solving an inverse problem that involves the forward model of the imaging system. To capture system non-idealities such as optical aberrations, a mainstream approach is to conduct an offline calibration scan to measure the system response for a point source at each spatial location on the imaging grid. However, it is challenging to run calibration scans when using structured SLMs as they cannot encode individual grid locations. In this study, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA). We introduce a piezo-stage that locomotes a pre-printed fixed coded aperture. A deep neural network is then leveraged to correct for the influences of system non-idealities in multiplexed measurements without the need for offline calibration scans. Finally, a deep plug-and-play algorithm is used to reconstruct images from corrected measurements. On simulated and experimental datasets, we demonstrate that CalibFPA outperforms state-of-the-art compressive FPA methods. We also report analyses to validate the design elements in CalibFPA and assess computational complexity.