Infrared fiber bundle image super-resolved based on computational imaging

Abstract

As the representative of flexibility in optical imaging media, in recent years, fiber bundles have emerged as a promising architecture in the development of compact visual systems. Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio (SNR) imaging in fiber bundles, the iterative super-resolution reconstruction network based on a physical model is proposed. Under the constraint of solving the two subproblems of data fidelity and prior regular term alternately, the network can efficiently “regenerate” the lost spatial resolution with deep learning. By building and calibrating a dual-path imaging system, the real-world dataset where paired low resolution (LR) - high resolution (HR) images on the same scene can be obtained simultaneously. Numerical results on both USAF target and complex target objects demonstrate that the algorithm can restore high contrast images without pixilated noise. On the basis of super-resolution reconstruction, compound eye image composition based on fiber bundle is also realized in this paper for the actual imaging requirements. The proposed work is the first to apply a physical model-based network structure to fiber bundle imaging in the long-wave infrared band, effectively promoting the engineering application of thermal radiation detection.