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.