局部背景变分迭代下的合成孔径声呐图像自适应均衡

针对合成孔径声呐图像存在的局部灰度畸变、对比度不高、目标被掩盖等问题，提出一种自适应均衡增强处理方法。首先，通过变分理论建立图像域非均衡时间演化模型；然后，利用声呐图像的局部信息及均衡时间演化过程中相邻时刻图像的差分关系，构建指数加权形式的均衡函数，并在迭代过程中进行权系数自动更新和背景分量估计；最后，通过背景均衡和动态范围调整得到均衡结果。实际数据验证和分析结果表明：均衡后的声呐图像背景变得更平整，对比度提高，目标与纹理得到增强，噪声等干扰也得到有效抑制；相较于对比算法，图像的局部均衡性、等效视数、峰值信噪比、局部结构相似性等均表现最优，实用性和有效性得到保证。

Adaptive equalization of synthetic aperture sonar image under local background variational iteration

Aiming at the problems of local gray distortion，low contrast，and target masking in synthetic aperture sonar images，this paper proposes an adaptive equalization enhancement method. Firstly，a non-equilibrium time evolution model in the image domain is established by the variational theory. Then the local information of the sonar image and the difference relationship between the images at adjacent moments in the equalization evolution process are used to construct the equalization function by means of exponential weighting. The weight coefficient is automatically updated，and the background component is estimated during the iteration. Finally，the equalization outcome is obtained by background equalization and dynamic range a-djustment. According to the verification and analysis of actual data，the background of the sonar image after equalization becomes more uniform，and the contrast is improved. In addition，the target and texture are enhanced，and the noise interference is effectively suppressed. Compared with other algorithms，the local equalization，equivalent view，peak signal-to-noise ratio，and local structure similarity of the image are optimal，and the practicability and effectiveness are guaranteed.