Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction.Methods: We modeled the MRI reconstruction problem with Bayes's theorem, following the recently proposed PixelCNN++ method. The image reconstruction from incomplete k-space measurement was obtained by maximizing the posterior possibility. A generative network was utilized as the image prior, which was computationally tractable, and the k-space data fidelity was enforced by using an equality constraint. The stochastic backpropagation was utilized to calculate the descent gradient in the process of maximum a posterior, and a projected subgradient method was used to impose the equality constraint. In contrast to the other deep learning reconstruction methods, the proposed one used the likelihood of prior as the training loss and the objective function in reconstruction to improve the image quality.Results: The proposed method showed an improved performance in preserving image details and reducing aliasing artifacts, compared with GRAPPA, 1 -ESPRiT, and MODL, a state-of-the-art deep learning reconstruction method. The proposed method generally achieved more than 5 dB peak signal-to-noise ratio improvement for compressed sensing and parallel imaging reconstructions compared with the other methods. Conclusion:The Bayesian inference significantly improved the reconstruction performance, compared with the conventional 1 -sparsity prior in compressed sensing reconstruction tasks. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.

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In this paper, we propose a reflective augmented reality (AR) display system based on integral imaging (II) using a mirror-based pinhole array (MBPA). The MBPA, obtained by punching pinholes on a mirror, functions as a three-dimensional (3D) imaging device, as well as an image combiner. The pinhole array of MBPA can realize a pinhole array-based II display, while the mirror of MBPA can image the real objects, so as to combine the images of the real objects with the reconstructed 3D images. The structure of the proposed reflective AR display is very simple, and only a projection system or a two-dimensional display screen is needed to combine with the MBPA. In our experiment, a 25cm × 14cm sized AR display was built up, a combination of a 3D virtual image and a real 3D object was presented by the proposed AR 3D display. The proposed device could realize an AR display of large size due to its compact form factor and low weight.

Aiming at completing search task under disaster condition problems, an optimizing strategy based on multi-sensor information fusion is proposed in this paper. Firstly, search and rescue robot control system hardware circuit is designed; secondly, embedded system software design is realized; and then, a polymerization Kalman filtering model is proposed, it uses local Kalman filter weights scheduling principle to improve system fault-tolerant ability and overall fusion performance. What’s more, Adaboost algorithm realizes the multi-sensor information optimal fusion. Through simulation test experiment, the robot search traversal ability is verified under unstructured environment

An optical see-through two-dimensional (2D)/three-dimensional (3D) compatible display using variable-focus lens and multiplexed holographic optical elements (MHOE) is presented. It mainly consists of a MHOE, a variable-focus lens and a projection display device. The customized MHOE, by using the angular multiplexing technology of volumetric holographic grating, records the scattering wavefront and spherical wavefront array required for 2D/3D compatible display. In particular, we proposed a feasible method to switch the 2D and 3D display modes by using a variable-focus lens in the reconstruction process. The proposed system solves the problem of bulky volume, and makes the MHOE more efficient to use. Based on the requirements of 2D and 3D displays, we calculated the liquid pumping volume of the variable-focus lens under two kinds of diopters.