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he Compressive Sensing (CS) framework has demonstrated improved acquisition efficiency on a variety of clinical applications. Of interest to this work is Reflectance Confocal Microscopy (RCM), where CS can influence a drastic reduction in instrumentation complexity and image acquisition times. However, CS introduces the disadvantage of requiring a time consuming and computationally intensive process for image recovery. To mitigate this, the current document details our preliminary work on expanding a Deep-Learning architecture for the acquisition and fast recovery of RCM images using CS. We show preliminary recoveries of RCM images of both a synthetic target and heterogeneous skin tissue using a state-of-the-art network architecture from compressive measurements at various undersampling rates. In addition, we propose an application-specific addition to an established network architecture, and evaluate its ability to further increase the accuracy of recovered CS RCM images and remove visual artifacts. Our initial results show that it is possible to recover compressively sampled images at near-real time rates with comparable quality to established computationally intensive and time-consuming optimization-based methods common in CS applications 

Compressive Sensing enables improvement of acquisition of a variety of signals in various applications with little to no discernible loss in terms of recovered image quality. The current work proposes a signal processing framework for the acquisition and fast reconstruction of compressively sampled hyperspectral images using an artificial neural network architecture. This ANN-based approach is capable of performing a fast reconstruction by avoiding the requirement of solving a computationally intensive image-specific optimization problem. The proposed framework contributes to advance single-pixel hyperspectral imaging device methodologies, which enable a significant reduction in device mechanical complexity, imaging rate, and cost. Our experiments demonstrate that a hyperspectral image can be reconstructed using only 10% of the samples without compromising classification performance. Specifically, the results show that classification performance of the compressively sampled hyperspectral image recovered using artificial neural networks is equal or higher to that of those obtained using current scanning hyperspectral imaging platforms.