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Small Target Detection Algorithm Based on Shearlet Transform and Neighborhood Difference

XIONG Shang-dao;YI Fan;HE Chao;YAN Zhao-jun;School of Physics Science and Technology, Wuhan University;  
A new algorithm based on shearlet transform is proposed for the detection of small target. The original image is decomposed by shearlet transform to obtain the original image details characteristics of multi-scale and multi-direction. Then, the low frequency subband is filtered by median filter to remove residual target. The weight coefficient of the high frequency subbands is adjusted according to the mean square error to suppress target and noise. The background prediction obtained by inverse shearlet transform is subtracted from an original image. Neighborhood Difference segmentation was used in the result image, in which target objects can be detected. The results demonstrate that the proposed method is more efficient than the bilateral filter.
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