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Abstract
We review the impact of deep-learning technologies on camera architecture. The function of a camera is first to capture visual information and second to form an image. Conventionally, both functions are implemented in physical optics. Throughout the digital age, however, joint design of physical sampling and electronic processing, e.g., computational imaging, has been increasingly applied to improve these functions. Over the past five years, deep learning has radically improved the capacity of computational imaging. Here we briefly review the development of artificial neural networks and their recent intersection with computational imaging. We then consider in more detail how deep learning impacts the primary strategies of computational photography: focal plane modulation, lens design, and robotic control. With focal plane modulation, we show that deep learning improves signal inference to enable faster hyperspectral, polarization, and video capture while reducing the power per pixel by $10 {-} 100\times$. With lens design, deep learning improves multiple aperture image fusion to enable task-specific array cameras. With control, deep learning enables dynamic scene-specific control that may ultimately enable cameras that capture the entire optical data cube (the “light field”), rather than just a focal slice. Finally, we discuss how these three strategies impact the physical camera design as we seek to balance physical compactness and simplicity, information capacity, computational complexity, and visual fidelity.
© 2020 Optical Society of America
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