Model-based deep learning for fiber bundle infrared image restoration
Bowen Wang, Le Li, Haibo Yang, Jiaxin Chen, Yuhai Li, Qian Chen, Chao Zuo
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 regularization 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 generated simultaneously. Numerical results on both the United States Air Force (USAF) resolution 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 embedded in this paper for the actual imaging requirements. The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band, effectively promoting the engineering application of thermal radiation detection.