Lensless-camera based machine learning for image classification
This work demonstrates the potential for non-human cameras in machine-based decision-making, but it is incremental as it applies existing methods to new data.
The paper tackled image classification using a lensless camera, achieving 99% accuracy for classifying two handwritten digits.
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.