Lensless object classification in long wave infrared using random phase encoding

Abstract

We introduce a lensless long wave infrared (LWIR) sensing system, utilizing double-random phase encoding. The employment of thin random phase encoding elements eliminates the need for traditional optical lenses. For object classification, convolutional neural network is used to process the speckle patterns produced by the random phase encoding, thus avoiding the reconstruction problem associated with lensless imaging. This approach is attractive for applications demanding compactness and cost-efficiency for LWIR systems. Experiments are provided to illustrate the proposed system. Our results demonstrate that this system competes well with conventional lensed LWIR imaging methods in a binary classification task under noisy conditions, where noise is not known a priori. To the best of our knowledge, this is the first report on such approaches in the LWIR domain.