The invention relates to a photo-thermal
microscopic imaging system and method based on a cyclic generative adversarial neural network, and belongs to the technical field of photo-thermal science and detection and
signal processing. Comprising a reflector, a sleeve lens, a first slide, a scanning lens, a platform, a
galvanometer controller, a dichroscope, a polarization cube, a reflector group, a second slide, a third slide, a spatial
light filter, an acousto-optic modulator, a first
optical filter, a polarization cube, a fourth slide, an exciting
light source, a polarization cube, a spatial
light filter and a second
optical filter. A probe
light source; and a photo-thermal
microscopic imaging system. According to the method, the three-dimensional
diffusion of the
heat flow is fully utilized, and the defect is accurately reconstructed from a two-dimensional angle by changing different spatial fluctuation states of the
heat flow, so that the detection depth and the detection efficiency are improved, and meanwhile, the quantitative characterization of the material and the three-dimensional
chromatography reconstruction of the geometric structure can be realized; according to the method, efficient and accurate detection and three-dimensional feature reconstruction of superficial layer defects of composite materials,
metal materials and high-molecular polymers are achieved.