19 September 2017 Edge detection for optical synthetic aperture based on deep neural network
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Synthetic aperture optics systems can meet the demands of the next-generation space telescopes being lighter, larger and foldable. However, the boundaries of segmented aperture systems are much more complex than that of the whole aperture. More edge regions mean more imaging edge pixels, which are often mixed and discretized. In order to achieve high-resolution imaging, it is necessary to identify the gaps between the sub-apertures and the edges of the projected fringes. In this work, we introduced the algorithm of Deep Neural Network into the edge detection of optical synthetic aperture imaging. According to the detection needs, we constructed image sets by experiments and simulations. Based on MatConvNet, a toolbox of MATLAB, we ran the neural network, trained it on training image set and tested its performance on validation set. The training was stopped when the test error on validation set stopped declining. As an input image is given, each intra-neighbor area around the pixel is taken into the network, and scanned pixel by pixel with the trained multi-hidden layers. The network outputs make a judgment on whether the center of the input block is on edge of fringes. We experimented with various pre-processing and post-processing techniques to reveal their influence on edge detection performance. Compared with the traditional algorithms or their improvements, our method makes decision on a much larger intra-neighbor, and is more global and comprehensive. Experiments on more than 2,000 images are also given to prove that our method outperforms classical algorithms in optical images-based edge detection.
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Wenjie Tan, Wenjie Tan, Mei Hui, Mei Hui, Ming Liu, Ming Liu, Lingqin Kong, Lingqin Kong, Liquan Dong, Liquan Dong, Yuejin Zhao, Yuejin Zhao, } "Edge detection for optical synthetic aperture based on deep neural network", Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039629 (19 September 2017); doi: 10.1117/12.2272922; https://doi.org/10.1117/12.2272922

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