The rear-view image system is one of the active safety devices in cars and is widely applied in all types of vehicles and traffic safety areas. However, studies made by both domestic and foreign researchers were based on a single image capture device while reversing, so a blind area still remained to drivers. Even if multiple cameras were used to expand the visual angle of the car’s rear-view image in some studies, the blind area remained because different source images were not mosaicked together. To acquire an expanded visual angle of a car rear-view image, two charge-coupled device cameras with optical axes angled at 30 deg were mounted below the left and right fenders of a car in three light conditions—sunny outdoors, cloudy outdoors, and an underground garage—to capture rear-view heterologous images of the car. Then these rear-view heterologous images were rapidly registered through the scale invariant feature transform algorithm. Combined with the random sample consensus algorithm, the two heterologous images were finally mosaicked using the linear weighted gradated in-and-out fusion algorithm, and a seamless and visual-angle-expanded rear-view image was acquired. The four-index test results showed that the algorithms can mosaic rear-view images well in the underground garage condition, where the average rate of correct matching was the lowest among the three conditions. The rear-view image mosaic algorithm presented had the best information preservation, the shortest computation time and the most complete preservation of the image detail features compared to the mean value method (MVM) and segmental fusion method (SFM), and it was also able to perform better in real time and provided more comprehensive image details than MVM and SFM. In addition, it had the most complete image preservation from source images among the three algorithms. The method introduced by this paper provided the basis for researching the expansion of the visual angle of a car rear-view image in all-weather conditions.