Mr. Yi Li Profile

Yi Li

at Beijing Institute of Technology

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Proceedings Article | 12 January 2018 Paper

Edge detection of optical subaperture image based on improved differential box-counting method

Yi Li, Mei Hui, Ming Liu, Liquan Dong, Lingqin Kong, Yuejin Zhao

Proceedings Volume 10620, 106200U (2018) https://doi.org/10.1117/12.2284776

KEYWORDS: Edge detection, Fractal analysis, Image enhancement, Image processing, Super resolution, Reconstruction algorithms, Image segmentation, Convolutional neural networks

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Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can’t be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

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