Improvement of image classification by multiple optical scattering

TL;DR

This paper demonstrates that multiple optical scattering can enhance image classification accuracy by transforming images into speckle patterns, effectively functioning as a feature extractor in a fast, low-power optical system suitable for edge computing.

Contribution

The study introduces an optical random scattering system that improves image classification accuracy and offers advantages like high speed, low power consumption, and miniaturization.

Findings

Achieved over 94% classification accuracy across various datasets.

Optical scattering acts as a feature extraction mechanism similar to neural networks.

System is suitable for real-time edge computing applications.

Abstract

Multiple optical scattering occurs when light propagates in a non-uniform medium. During the multiple scattering, images were distorted and the spatial information they carried became scrambled. However, the image information is not lost but presents in the form of speckle patterns (SPs). In this study, we built up an optical random scattering system based on an LCD and an RGB laser source. We found that the image classification can be improved by the help of random scattering which is considered as a feedforward neural network to extracts features from image. Along with the ridge classification deployed on computer, we achieved excellent classification accuracy higher than 94%, for a variety of data sets covering medical, agricultural, environmental protection and other fields. In addition, the proposed optical scattering system has the advantages of high speed, low power consumption, and miniaturization, which is suitable for deploying in edge computing applications.