# Lensless-camera based machine learning for image classification

Source: [Lensless-camera based machine learning for image classification](<https://www.sota2.com/research/paper/68fa36441e209131ce8a880e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-24T21:44:03.328Z

Authors: Ganghun Kim, Stefan Kapetanovic, Rachael Palmer, Rajesh Menon

Published: 2017-09-03T23:42:29.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1709.00408>)

[PDF](<https://arxiv.org/pdf/1709.00408.pdf>)