Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain

Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2015, Article ID 493142, 14 pages http://dx.doi.org/10.1155/2015/493142 Research Article Automatic Side-Scan Sonar Image Enhancement in Curvelet Transform Domain Yan Zhou,1 Qingwu Li,1 and Guanying Huo1,2 1 Key Laboratory of Sensor Networks and Environmental Sensing, Hohai University, Changzhou 213022, China Advanced Robotics and Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON, Canada N1G 2W1 2 Correspondence should be addressed to Qingwu Li; li Received 16 January 2015; Revised 23 March 2015; Accepted 23 March 2015 Academic Editor: Gisele Mophou Copyright © 2015 Yan Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose a novel automatic side-scan sonar image enhancement algorithm based on curvelet transform. The proposed algorithm uses the curvelet transform to construct a multichannel enhancement structure based on human visual system (HVS) and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Firstly, the noisy and low-contrast sonar image is decomposed into a low frequency channel and a series of high frequency channels by using curvelet transform. Secondly, a new nonlinear mapping scheme, which coincides with the logarithmic nonlinear enhancement characteristic of the HVS perception, is designed without any parameter tuning to adjust the curvelet transform coefficients in each channel. Finally, the enhanced image can be reconstructed with the modified coefficients via inverse curvelet transform. The enhancement is achieved by amplifying subtle features, improving contrast, and eliminating noise simultaneously. Experiment results show that the proposed algorithm produces better enhanced results than state-of-the-art algorithms. 1. Introduction Acoustic remote sensing technologies, such as high-resolution multibeam and side-scan sonars imaging in water, are widely used in marine geology, commercial fishing, offshore oil prospecting and drilling, and so forth [1–4]. Due to transmission loss and acoustic wave scattering, sonar images are notorious for low contrast, edge-blurring, and being full of noise. Therefore it is necessary to amplify faint edges and eliminate noise in sonar images simultaneously for further image processing, such as image segmentation and object detection and classification. Image enhancement approaches can generally be divided into two categories: spatial domain methods and transform domain methods. Spatial domain enhancement methods deal with the image pixels. Desired enhancement can be achieved by manipulating the pixel values. Commonly-used spatial techniques are linear stretch, histogram equalization (HE) [5], convolution mask enhancement, adaptive histogram equalization, and so forth. The conventional histogram equalization has received considerable attention due to its simple and straightforward implementation, but it often amplifies noise, blurs subtle edges, and tends to over-enhance the image contrast if there are high peaks in the histogram [6]. These spatial domain methods usually cannot effectively discriminate edges from noise, because edges and noise have similar properties in spatial domain. One way to solve this problem is to use multiscale geometric analysis (MGA) to decompose the image into different frequency bands and process the image in each band independently. It belongs to transform domain methods, the second category. Multiscale wavelet-based image enhancement algorithms have achieved promising results over the last decades [7, 8]. However, two-dimensional (2D) wavelet transform commonly used is a separable extension of 1D wavelet transform, which does not work very well in capturing the image’s geometric edges because of its isotropy. To overcome the limitation of the wavelet transform, other multiscale analyses have been developed during the past decade, including curvelet transform [9] and nonsubsampled contourlet transform (NSCT) [10]. These approaches capture edges better than the wavelet transform owing to 2 their high directional sensitivity and anisotropy. The curvelet transform therefore has been widely applied in the image processing field [11–15]. A contrast enhancement method based on curvelet transform has been developed, which uses a gain function with four parameters to modify the curvelet transform coefficients [11]. However, it requires appropriate manual parameter settings for different images that might otherwise result in image degradations. Lu et al. [16] proposed a piecewise function based enhancement method in curvelet transform domain (PFBE) to enhance the sonar image’s contrast. This method reduces the complexity of parameter adjustment by using an improved gain function with only one parameter, but it still requires parameter selection, which is manually set according to the input sonar images. In order to avoid manual parameter tuning, an automatic image enhancement method based on NSCT (AIE-NSCT) is proposed, which adjusts the NSCT coefficients by using a nonlinear mapping function [17]. This state-of-the-art image enhancement method has achieved good results in both grayscale and colour images. When processing the sonar image which has very low signal-to-noise ratio and strong noise, AIE-NSCT cannot sufficiently adjust contrast and eliminate noise. Furthermore, owing to the high redundancy of NSCT, NSCT-based methods are more time-consuming than curvelet-based methods. Curvelet transform is better in representing edges and removing noise than classical wavelet transform for its anisotropy and multidirectional decomposition capabilities, and it is also faster than many other multiscale geometric transforms for its less redundancy. Moreover, curvelet transform well coincides with the sparse coding mechanism and the multichannel processing mechanism of the human visual system (HVS), which is composed of a series of parallel channels with each channel corresponding to a specific range of image spatial frequencies. Therefore, we propose an automatic side-scan sonar image enhancement method based on curvelet transform in this paper. The proposed algorithm utilizes the curvelet transform to model a multichannel enhancement structure based on the HVS and adopts a new adaptive nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently and automatically. Experiment results show that the proposed method can effectively enhance the contrast while eliminating noise and preserving edges in side-scan sonar images. The proposed method outperforms the state-of-the-art enhancement techniques in both qualitative and quantitative assessments. The remainder of this paper is or (...truncated)