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Lensless-camera based machine learning for image classification
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Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.
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Machine-learning enables Image Reconstruction and Classification in a "see-through" camera
A U-net reconstructs MNIST, EMNIST, and Kanji49 images from raw sensor data of a see-through lensless camera, but classification benefits are inconsistent and the manuscript is incomplete.
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