针对水下双目图像匹配时不再满足空气中极线约束条件以及归一化互相关(NCC)算法处理水下图像计算量大等问题，提出了一种基于最佳搜索域的水下图像区域匹配算法。对双目摄像机进行标定得到相关参数，并获取参考图和待匹配图；运用曲线极线约束计算出与空气中极线的最大偏离值，确定最佳搜索域；用NCC进行匹配，将原来的线性搜索改为在最佳搜索域中进行多行搜索，提高匹配精度；并应用盒滤波技术加速，提高匹配速度。实验结果表明，该算法达到了尺度不变特征变换(SIFT)算法的匹配精度，可以应用在整幅图中进行稠密匹配，且运算速度比原有NCC匹配算法大大提高，成功将区域匹配算法应用于水下环境中。

In terms of underwater binocular image matching cannot satisfy the epipolar constraint of air, and the large amount of calculation of underwater image processed by the normalized cross correlation (NCC) algorithm, an underwater region matching algorithm based on optimum searching area is presented. Binocular camera should be calibrated in order to obtain some relevant parameters, as well as reference image and image to be matched; the maximum deviate value from the line in the air can be calculated through the curve constraint and the optimum searching area is therefore decided. The NCC region matching algorithm can help to match two images, at the same time, instead of searching on the original epipolar line, an optimum searching area is proposed so that the searching is performed in this area with several lines to achieve the purpose of a higher accuracy. Meanwhile, the time spent on the matching is reduced by the application of box filter technology. The results of the test indicate this algorithm achieves the same matching accuracy compared with the scale-invariant feature transform (SIFT) feature matching algorithm and this can be used to perform dense disparity. Also the speed of matching is largely accelerated compared with the original NCC algorithm. Therefore, the region matching algorithm is successfully applied to underwater image matching.