Ghost Imaging with Deep Learning for Position Mapping of Weakly Scattered Light Source
Yasuhiro Mizutani, Shoma Kataoka, Tsutomu Uenohara, Yasuhiro Takaya
Abstract
Abstract We propose ghost imaging (GI) with deep learning to improve detection speed. GI, which uses an illumination light with random patterns and a single-pixel detector, is correlation-based and thus suitable for detecting weak light. However, its detection time is too long for practical inspection. To overcome this problem, we applied a convolutional neural network that was constructed based on a classification of the causes of ghost image degradation. A feasibility experiment showed that when using a digital mirror device projector and a photodiode, the proposed method improved the quality of ghost images.
Topics & Concepts
Ghost imagingArtificial intelligenceComputer scienceComputer visionConvolutional neural networkProjectorDetectorDeep learningPosition (finance)PixelPattern recognition (psychology)OpticsPhysicsEconomicsTelecommunicationsFinanceRandom lasers and scattering mediaAdvanced Optical Imaging TechnologiesOptical Coherence Tomography Applications