We propose a novel automatic sidescan 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 lowcontrast 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 stateoftheart algorithms.
Acoustic remote sensing technologies, such as highresolution multibeam and sidescan sonars imaging in water, are widely used in marine geology, commercial fishing, offshore oil prospecting and drilling, and so forth [
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. Commonlyused spatial techniques are linear stretch, histogram equalization (HE) [
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 waveletbased image enhancement algorithms have achieved promising results over the last decades [
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 sidescan 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 sidescan sonar images. The proposed method outperforms the stateoftheart enhancement techniques in both qualitative and quantitative assessments.
The remainder of this paper is organized as follows. Section
Curvelets were first introduced by Candès and Donoho in 1999 [
Assume that we work throughout in two dimensions, that is,
Define a “mother” curvelet as
Introduce the equispaced sequence of rotation angles
Curvelet tiling of frequency domain and space domain.
In practical implementation, we define Cartesian window by
The digital curvelet tiling.
Thus, the discrete curvelet transform is defined as
Two versions of fast discrete curvelet transform (FDCT), namely, FDCT via Unequispaced FFTs (USFFT) and FDCT via Wrapping, were developed [
It is well known that for the HVS, the receptive fields of simple cells in primary visual cortex can be characterized as being spatially localized, oriented, and bandpass [
As a multiscale multidirectional transform, the curvelet transform allows an almost optimal nonadaptive sparse representation of objects with edges. Because the curvelet transform exactly coincides with the mechanism of human visual perception, we can model the multichannel enhancement structure using the curvelet transform. The low frequency subband of the curvelet transform corresponds to the lowpass channel of the HVS, and each high frequency directional subband corresponds to each bandpass channel. Figure
Curvelet decomposition of a sidescan sonar image. (a) Original image. (b) Reconstruction of low frequency subband. (c)–(e) Reconstruction of high frequency subband: scales from coarse to fine.
Because sonar images are full of strong noise, the critical problem for sonar image enhancement is to effectively remove noise while adaptively adjusting dynamic range and amplifying weak edges. After curvelet decomposition on a sonar image, the low frequency subband, which is almost noiseless, contains overall contrast information. While the high frequency subband in each scale and direction contains not only edges but also noise. Edges are geometric structures, while noise is not, so we can use the curvelet transform to distinguish edges from noise. Consequently, the low frequency subband, which corresponds to the lowpass channel of the HVS, needs to be stretched appropriately. However each high frequency subband, which corresponds to each bandpass channel of the HVS, needs to be sufficiently enhanced with denoising. According to the fundamental requirements proposed by Laine et al. in [
low contrast area should be enhanced more than high contrast area;
sharp edges should not be blurred;
the nonlinear function should be monotonically increasing, in order to maintain the location of local extrema and avoid generating new extrema;
the nonlinear function should be antisymmetric, for example,
To achieve the adaptive multichannel enhancement, we propose a nonlinear mapping scheme to modify the curvelet transform coefficients in each channel independently.
For the low frequency subband (
Subjective brightness (intensity as perceived by the HVS) is a nonlinear logarithmic function of the light intensity incident on the eye, as shown in Figure
The nonlinear logarithmic property of the HVS.
Mapping curve of curvelet transform coefficients for Figure
The low frequency subband
The high frequency subband (
For the high frequency subband in each scale and direction
Because the shapes of mapping curves in all the high frequency subbands are similar, the mapping curve of the high frequency subband indexed by
In summary, the proposed nonlinear mapping achieves the following targets: preserving strong edges by keeping the large coefficients, enhancing weak edges by amplifying the small coefficients, and removing noise by eliminating noise coefficients using thresholding in the high frequency subbands
A block diagram of the proposed image enhancement algorithm is shown in Figure
Block diagram of the proposed image enhancement algorithm.
Input: original image
Implement the curvelet transform of
Calculate the enhanced curvelet transform coefficients
Estimate the noise standard deviation
Calculate the enhanced curvelet transform coefficients
Reconstruct the enhanced image from the coefficients
Output: enhanced image
In this section, the effectiveness of the proposed algorithm is validated through computer simulation. We compare our algorithm with three image enhancement algorithms: HE [
Four typical sidescan sonar images, shown in Figure
Original sidescan sonar images. (a)
Enhancement results for image
Enhancement results for image
Enhancement results for image
Enhancement results for image
Mapping curves of enhanced images. Key: green solid line, nochange mapping; cyan solid line, HE; red dashdotted line, AIENSCT; blue solid line, PFBE; black dashed line, the proposed algorithm.
The original image
The original image
The original image
The original image
The corresponding edge detection results of the enhanced images are shown in Figures
To acquire quantitative evaluation of enhancement results, the image contrast measure called the measure of enhancement by entropy or EME using entropy is proposed in [
The EME by entropy (EMEE), which is of the entropy formula form XlogX, is actually measuring the entropy, or information, in the contrast of the image [
Comparison of EMEEs.
Image  Original  HE  AIENSCT  PFBE  Proposed 


233.22  10.42  8591.70  2029.00  31008.00 

582.06  3.21  529.90  1558.00  2173.50 

233.59  4977.50  699.02  614.95  12465.00 

458.23  1447.20  2296.50  881.44  4085.70 
All the algorithms are implemented under MATLAB R2011b environment on a PC with 3 GHz Pentium(R) Dualcore CPU E5700 and 2 GB RAM. The running times of the four methods are given in Table
Comparison of running time (s).
Image  HE  AIENSCT  PFBE  Proposed 


0.03  81.97  2.52  1.13 

0.02  21.03  0.75  0.33 

0.03  79.80  2.46  1.11 

0.02  28.73  0.97  0.48 
In this study, a new automatic sidescan sonar image enhancement algorithm in curvelet transform domain is proposed. We present an adaptive multichannel enhancement structure based on the HVS, combining the nonlinear mapping scheme with the curvelet transform. The proposed nonlinear mapping scheme is well designed to achieve the following goals: in the high frequency subbands, amplifying the coefficients of weak edges, preserving the coefficients of strong edges, and inhibiting noise coefficients, and in the low frequency subband, adjusting the dynamic range adequately. The nonlinear mapping is adaptive without any parameter tuning and is consistent with the nonlinear logarithmic property of the HVS. Therefore, the proposed algorithm can automatically achieve noise suppressing, edge sharpening, and contrast enhancement for sidescan sonar images. The proposed algorithm is tested on real sonar images and is compared with some popular enhancement algorithms. Experiment results demonstrate that the proposed algorithm outperforms the existing enhancement algorithms in terms of subjective visual evaluation and objective quantitative evaluation measure of EMEE. Moreover, compared with HE, the proposed algorithm can enhance the image much better with only a bit more time consumption. Compared with NSCTbased enhancement algorithm, our algorithm not only produces better results but also consumes much less time. Compared with PFBE, which is a nonadaptive curveletbased enhancement algorithm, our algorithm can achieve better enhancement results without adjusting parameters manually. Therefore, the proposed approach can be easily and effectively used for sonar image enhancement.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported by the National Natural Science Foundation of China (no. 60972101 and no. 41306089) and the Natural Science Foundation of Jiangsu Province (no. BK20130240).