Speckle-learning-based object recognition using optical memory effect

Abstract

We present an efficient construction method for object recognition based on speckle learning using the optical memory effect. An object classifier based on speckle learning without the process of reducing or eliminating scattering and with a simple optical setup has been previously reported, but it requires a large number of training images to improve the performance of the classifier. This method is not applicable for bioimaging because of the difficulty of collecting training images caused by position control and phototoxicity of target cells. In our method, a wide variety of training images are augmented by a computer from a few speckle intensity images in the working range of the optical memory effect. We experimentally demonstrated our method with a 4f-optical system implementing the optical memory effect. As a result, the constructed binary classifier showed high accuracy under various scattering conditions and resolutions of the test image.