Efficient edge-preserved sonar image enhancement method based on CVT for object recognition

摘要

In the field of computer-aided recognition, edge feature is one of the key factors to determine recognition performance. Comparing to an optical image, since sonar image via acoustic wave is easily influenced by underwater environments such as particle density, temperature, and current, edge information should be boosted. Some image preprocessing techniques based on transform domain such as wavelet and curvelet may be good candidates but conventional methods show not only the possibility of enhancing edge features but also the limitation due to the absence of consideration to the edge direction. This study proposes an improved edge enhancement method based on curvelet transform (CVT), which is able to find out edge direction. The proposed method (PM) calculates the maximum value by ridgelet coefficients on each angular line, derived from the sub-step of the CVT, and the real edge direction is determined by local maxima selection after finding the azimuth of this value. In addition, selective sharpening is performed according to the feature information of edge. Experimental results have shown that the PM is comparable with conventional methods in terms of edge intensity, recognition rate, and peak signal-to-noise ratio.