Volume Multi-Source Images Registration Based on Improvement Model Fitting and Adjustment

In order to improve the accuracy of volume multi-source images registration, a volume multi-source images registration was proposed based on improvement model fitting and adjustment (VMIRIMFA). Firstly, fast adaptive robust invariant scalable feature detector (FARISFD) and robust overlapped gauge feature descriptor (ROGFD) was used to enhance the robustness of feature registration. Then, improved random sample consensus was proposed to ensure the robustness of the algorithm and to improve the operation efficiency. Finally, an adjustment method was proposed for volume multi-source images registration and result optimization. The experiment results show significant advantages of VMIRIMFA in terms of precision for multi-source images with the large difference.