Searching Moving Objects by Image Correlation

In the field of astronomical image processing, image subtraction techniques are widely used. Due to the varying seeing conditions, different exposure times, and different filters, image subtraction cannot be performed on two images. A novel image subtraction algorithm is proposed in the paper. Its essence is based on correlation to eliminate the parts with the similar flux distribution and preserve the parts with the different flux distribution between two images in sense of statistics. The algorithm can be fast executive, is numerically stable, and is locally independent. Based on our algorithm, we have developed a set of image subtraction code with Python as the interface and C as the implementation. We use the algorithm, together with three other similar algorithms, to perform with the astronomical images. Experiments show that our algorithm can find the difference between two images and detect moving objects in a very short time while having good robustness and position measurement stability.