In recent years, deep learning has been shown to be one of the leading machine learning techniques for a wide variety of inference tasks. In addition to its mainstream applications, such as classification, it has created transformative opportunities for image reconstruction and enhancement in optical microscopy. Some of these emerging applications of deep learning range from image transformations between microscopic imaging systems to adding new capabilities to existing imaging techniques, as well as solving various inverse problems based on microscopy image data. Deep learning is helping us move toward data-driven instrument designs that blend microscopy and computing to achieve what neither can do alone. This article provides an overview of some of the recent work using deep neural networks to advance computational microscopy and sensing systems, also covering their current and future biomedical applications.

Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a two-dimensional separable sensing operator. By so doing, we reduce the complexity by factor of 106for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.

An image sensor architecture with an alternative image scan method, based on Morton (Z) order, is presented. This scan, compared to the conventional row scan, enables faster and efficient average computation of square image blocks. Digital averaging is used and the pixel data is read out with either the original resolution, a 2 /spl times/ 2 or a 4 /spl times/ 4 block averaging. A test chip of 128 /spl times/ 128 array has been implemented in 0.35-/spl mu/m CMOS technology, has 15% fill factor, is operated by a 3.3-V supply and dissipates 30 mW at video rate.