Redefining frontiers of computational imaging with deep learning

Abstract

In recent years, the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms. Traditional methods encounter significant challenges when reconstructing high-dimensional information in complex scenarios[1]. By leveraging the powerful nonlinear modeling and advanced feature extraction capabilities of deep learning, these barriers have been effectively overcome, enabling end-to-end optimization—from optical system design to image reconstruction[2]. This shift transforms optoelectronic imaging from a conventional “what you see is what you get” model toward a more adaptive “what you see is what you need” approach, catalyzing breakthroughs across diverse applications including optical imaging, medical diagnostics, remote sensing, and beyond. In a seminal review recently published in Photonics Insights[3], Luo et al. systematically outlined the pivotal role of deep learning and its latest advancements within computational imaging. Figure 1 provides a schematic overview of the key concepts of the review. Their comprehensive analysis highlights how deep learning methodologies are redefining imaging technology across three transformative domains: computational optical system design, high-dimensional light-field decoding, and advanced image processing and enhancement.