Abstract
According to maritime image histograms' statistic and analysis, the histogram of pure maritime image obeys Gaussian distribution approximately, thus Three Adaptive Sub-histograms Equalization (TASHE) algorithm for maritime image enhancement is proposed in this paper. First, the characteristics of pure maritime image's histogram are studied, then the adaptive threshold's optimal selection strategy for the histogram's division is discussed, finally the implement of three sub-histograms is described. This paper employs visible gray maritime image, visible color maritime image and infrared maritime image to verify the enhancement algorithm's effectiveness and robustness, the experimental results show that TASHE algorithm can not only keep the maritime image's mean brightness and naturalness, but also improve the maritime image's contrast without the noise and artifacts. The objective image quality assessment also indicates that TASHE algorithm can improve the original maritime image's Enhancement Measure by Entropy (EME) value, furthermore, when a maritime image is pre-processed by TASHE algorithm, the maritime target's Detection Rate (DR) can be improved.
Highlights
Histogram Equalization (HE)[1] is one of the most widely used methods in image enhancement[2,3,4,5], which has the advantages of easy computation and implementation[6].the result after histogram equalization brings about some disadvantages[7] such as mean gray value’s fixing, entropy’s declination, and the details’ missing
The objective image quality assessment indicates that Three Adaptive Sub-histograms Equalization (TASHE) algorithm can improve the original maritime image’s Enhancement Measure by Entropy (EME) value, when a maritime image is pre-processed by TASHE algorithm, the maritime target’s Detection Rate (DR) can be improved
IV, it can be seen that the proposed algorithm has the largest EME value, and the Mean Brightness (MB) value of the proposed algorithm is the nearest to the original image’s among the compared algorithms, it can be concluded that our proposed algorithm can keep the original maritime image’s brightness, and enhance the maritime target
Summary
Histogram Equalization (HE)[1] is one of the most widely used methods in image enhancement[2,3,4,5], which has the advantages of easy computation and implementation[6].the result after histogram equalization brings about some disadvantages[7] such as mean gray value’s fixing, entropy’s declination, and the details’ missing. Some researchers study the sub-histogram equalization methods, which can enhance the image and prevent the overenhancement, for example, Brightness Preserving BiHistogram Equalization (BBHE)[8] first divides the image histogram into two parts(sub-histograms), and the two sub-histograms are independently equalized, which can preserve the original image’s mean brightness. Some researchers have studied the modified histogram equalization methods named by weighted histogram technique, thresholded histogram technique, or clipped histogram technique, histogram equalization operates the new histogram, the enhancement result usually has a better performance, this is because the pixel’s frequency of histogram can control the image’s enhancement rate. In the reformed process of HE technique, image’s characteristics can be used as the dividing threshold, for example, exposure based Sub Image Histogram Equalization (ESIHE)[13] combines the idea of BBHE and BHEPL, which takes the image’s exposure as the threshold to divide the
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