Lensless-camera based machine learning for image classification

Abstract

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.

Cited by in corpus (5)

• Inverse-designed flat lens for imaging in the visible & near-infrared with diameter > 3mm and NA=0.3
• A needle-based deep-neural-network camera
• Optics-free imaging of complex, non-sparse QR-codes with Deep Neural Networks
• Imaging-free object recognition enabled by optical coherence
• Classification of optics-free images with deep neural networks