Improvement of Image Classification by Multiple Optical Scattering

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 liquid crystal display (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.

Automated summary

The study utilized an optical random scattering system based on LCD and RGB laser source to improve image classification accuracy, achieving excellent classification accuracy of over 94% through ridge classification, suitable for data sets in medical, agricultural, environmental protection, and other fields. The proposed optical scattering system has the advantages of high speed, low power consumption, and miniaturization, making it suitable for edge computing applications.