## 5 Discussion
Therefore, compared with the AW before the improvement, the false matching points of its matching results are reduced, and the matching effect is better in the edge region of color change. As shown in Table 4, for two groups of heart model images, the false matching rates of the algorithm before the improvement are 11.74% and 13.17%, respectively, with an average false matching rate of 12.46%, while the improved algorithm is 6.13% and 5.17%, respectively, with an average error-matching rate of 5.92%. Combined with the previous analysis results, the matching accuracy of the heavy stereo matching algorithm of the improved joint similarity measure and adaptive weight has been greatly improved, and the overall matching effect is better. As can be seen from Figure 8, the first column is the left image in the two groups of heart model images, the second column is the corresponding standard parallax image, and the third column is the final result parallax image corresponding to the two groups of heart model images, and the fourth column is the mismatch distribution corresponding to the final result parallax image. According to the result parallax image and the distribution image of their mismatching points, the mismatching points are mainly distributed in the curved surface area and the area with serious illumination distortion. However, according to the result parallax image, the parallax corresponding to the mismatching points in the illumination distortion area is not very different from the standard parallax value, which shows that the algorithm is robust to illumination distortion. The distribution of mismatched points in the surface area is in the form of stepped small stripes, which are mainly caused by the relatively gentle change of the surface. That is, there is a certain change between the corresponding visual difference of adjacent pixels, but its change value is very small. During the implementation of the algorithm, a single pixel should be taken as the minimum unit, and then sub-pixel enhancement should be carried out according to the matching cost so as to obtain a sub-pixel parallax image. Therefore, there will be some errors in the layered areas in the obtained unprocessed parallax image, so this situation will occur. However, by comparing the standard parallax image with the final result parallax image, it can be seen that the deviation between these mismatched points and the real disparity is still small. That is, the corresponding matching effect of this algorithm is suitable. To verify the effectiveness of the post-processing process, the corresponding error-matching rate of the before and after post-processing vision images is counted. As shown in Table 5, the error-matching rate of the sub-vision image is significantly reduced by the post-processing process. That is, the post-processing operation is desirable. After combining the error-matching point distribution of after post-processing parallax figure, it can be seen that the dense vision images obtained by this algorithm are low, and the results figure obtained are basically in line with the image. That is, it can obtain a suitable dense vision image of the heart soft tissue image through this algorithm. According to the average mismatch rate of each algorithm when combining images with different brightness, when the brightness of the left and right images is different, the corresponding mismatch rate of CAD+AW reaches more than 80%, which basically loses its function.