Improvement of image classification by multiple optical scattering
This work addresses image classification for edge computing applications by offering a high-speed, low-power optical system, though it appears incremental as it combines existing scattering concepts with ridge classification.
The paper tackled image classification by using multiple optical scattering as a feedforward neural network to extract features, achieving over 94% accuracy across diverse datasets including medical and environmental fields.
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.