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.

我们提出了一种基于利用光学记忆效应的散斑学习的物体识别的有效构造方法。之前已经报道过一种基于散斑学习的物体分类器，无需减少或消除散射的过程，并且具有简单的光学设置，但它需要大量的训练图像来提高分类器的性能。该方法不适用于生物成像，因为目标细胞的位置控制和光毒性导致训练图像收集困难。在我们的方法中，计算机从光学记忆效应工作范围内的一些散斑强度图像中增强了各种训练图像。我们通过实现光学记忆效应的 4f 光学系统实验证明了我们的方法。结果，构建的二元分类器在各种散射条件和测试图像分辨率下均表现出较高的准确度。