Affiliation:
1. School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China
2. Beijing Institute of Space Mechanics & Electricity, Beijing 100094, China
Abstract
In the field of compressed imaging, many attempts have been made to use the high-resolution digital micromirror array (DMD) in combination with low-resolution detectors to construct imaging systems by collecting low-resolution compressed data to reconstruct high-resolution images. However, the difficulty of achieving micrometer-level alignment between DMD devices and detectors has resulted in significant reconstruction errors. To address this issue, we proposed a joint input generative adversarial network with an error correction function that simulates the degradation of image quality due to alignment errors, designed an optical imaging system, and incorporated prior imaging system knowledge in the data generation process to improve the training efficiency and reconstruction performance. Our network achieved the ability to reconstruct 4× high-resolution images with different alignment errors and performed outstanding reconstruction in real-world scenes. Compared to existing algorithms, our method had a higher peak signal-to-noise ratio (PSNR) and better visualization results, which demonstrates the feasibility of our approach.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Jilin Province
Foundation strengthen domain technology fund
Scientific and technological research projects of The Education Department of Jilin Province
Strategic Research Issues of Beijing Institute of Space Mechanics & Electricity
Center of Space Exploration, Ministry of Education
Subject
Radiology, Nuclear Medicine and imaging,Instrumentation,Atomic and Molecular Physics, and Optics
Cited by
1 articles.
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