A Research On Quick-Sift And Ghosting Elimination Technique For Uav Image Mosaic

The practical value of unmanned aerial vehicles (UAVs) can be improved by using image mosaic to fill the gaps due to insufficient coverage of UAV aerial images. Research on UAV image mosaic is majorly focused on improving the operation speed and mosaic accuracy. To this end, this study first analyzes the UAV aerial image characteristics and causes of ghosting and blurring of images. A Quick-Scale-Invariant Feature Transform (Quick-SIFT) operator is constructed to reduce computer time by reducing the octaves and levels of the Gaussian pyramid and selecting the third level image in each octave for feature point extraction. Subsequently, the As-Natural-As-Possible (AANAP) algorithm is used for image registration and projection transformation. The difference region between adjacent sequence images is calculated by the frame difference method. The difference region thus obtained is subjected to the region growing algorithm to segment the moving objects by single sampling, the other regions are processed by linear weighted fusion, thereby eliminating ghosting effectively. Lastly, A special aerial images dataset for image mosaic is constructed, based on which the comparable experiments with state-of-art image mosaic methods are conducted. The experimental results indicate that the matching time with the proposed algorithm is improved by at least 78% compared to SIFT, thus realizing fast image mosaic processing. The proposed algorithm also effectively eliminates the motion ghosting in images and achieves stable, highquality results.