This paper proposes a novel network architecture to integrate features in the shallow and the deep layers. The architecture is composed of three components, including the shortcut connection, global representation operator, and regularizer. The shortcut connection alleviates the vanishing gradient problem by feeding features from the hidden layer to the classifier in the last layer. The global representation operator aims to reduce the features’ dimension to mitigate the over-fitting problem. The regularizer is designed to relieve the degradation of network depth which might be caused by the shortcut connection. The results on Optical Coherence tomography (OCT) show that the proposed architecture is an effective regularization method that can mitigate the over-fitting problem.
In order to theoretically analyze and estimate decorrelation phase error in digital holographic interferometry, the principle of digital holographic imaging system is introduced in this paper, and general point spread function (PSF) of digital holographic system is derived and its approximate function is obtained. According to the characteristics of the digital holographic imaging in accordance with the laws of statistical optics, the expression of complex amplitude standard deviation of σA, σB and σC in each region of the double exposure time and the relationship between the degree of decorrelation are derived, and the expression of the phase error of decorrelation is given. It is simulated in MATLAB, simulative results indicate that statistical properties of decorrelation phase error obtained through theory analysis correspond to decorrelation phenomenon. And the measuring condition, in digital holography interferometry, which decorrelation degrees between the holographies of every double exposure should satisfy ρx + ρy <0.1, is derived.
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