Journals >Laser & Optoelectronics Progress
ing at the loss of information and edge blurring during texture recovery using super-resolution technology based on convolution neural networks, we combine dense block and squeeze module to learn the mapping from low-resolution to high-resolution in an end-to-end manner. The dense block structure formed by the fusion of dense connection utilizes context information of image region effectively. The squeeze module amplifies valuable global information selectively and suppresses the useless features. The multiple 1×1 convolution layer structures in the image reconstruction section reduce the dimension of the previous layers, and speed up the calculation while reducing the loss of information. Processing the original image directly shortens the training time, and the optimization of convolution layers and filters reduces the computational complexity significantly.
.ing at the problem of insufficient sweep range of the swept source used in the existing swept source optical coherence tomography (SS-OCT), a Fourier domain mode-locking(FDML) high-speed broadband swept source based on a quantum dot semiconductor optical amplifier (QD-SOA) and a quantum well semiconductor optical amplifier (QW-SOA) in parallel is studied. The output characteristics of two types of SOA are studied, and the QW-SOA with a center wavelength of 1310 nm and the QD-SOA with a center wavelength of 1280 nm are placed in parallel in the fiber annular cavity. A high-speed broadband swept source is developed combined with FDML. The sweep range is 318 nm, the full width at half maximum is 110 nm, the sweep speed is 101 kHz, the average output optical power is 7.8 mW, and the instantaneous linewidth is less than 0.1 nm, respectively.
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