Iterative algorithm for offsets, scale and rotate estimation for television image superposition with additive and multiplicative noise

We describe the iterative algorithm for television image superposition. The superposition is defined by offsets, scale, and rotates. Also additive and multiplicative noise influences the image. The main aim of developing this algorithm is to reduce the time of processing images for estimation superposition parameters. Reducing processing time is provided by reducing the set of reference points, which defines the superposition. The initial coordinate of the reference points is refined at the process of the algorithm work for acceptable superposition of the television images. The superposition parameters are divided into two groups. Offsets belong to the first group, scale and rotate belong to the second group. The parameters in each group are estimated independently. The iterative procedure uses the offsets for estimation scale and rotate, and after it uses scale and rotates for estimation of the offsets. This process is repeated. The next iteration approximates the rate to the real value of the superposition parameters. The developed algorithm allows reducing processing time at 25 times faster than the brute force algorithm for the test data. The test data include two images; the first image has the resolution 288 × 384 pixels, the second image has the resolution 128 × 128 pixels. The second image is the fragment of the first image. Also at the end of the article, the numerical simulation had been presented. The simulation shows the dependences of error estimation of parameters from the noise power.