Compared with ground views and direct overhead views (for orbital satellites), aerial robotics allow for capturing videos from diverse viewpoints and scenes, thus, the content of aerial image is complex and changeable, and aerial video has complex inter-frame transforms stemming from the blend of camera motion, platform motion and jitter. In addition, poor quality and similar texture are common in longdistance and large-scale aerial video surveillance. All of these interferences make image registration of aerial video difficult. This paper puts forward one image registration method suited to aerial video on the basis of the hypothesize-and-verify of RANSAC. The proposed accelerated RANSAC, named PSSC-RANSAC (Prior Sampling & Sample Check RANSAC), incorporates prior sampling, which comes from three levels of sample evaluation, including texture magnitude, spatial consistency and feature similarity, to generate more possibly correct samples in priority. Furthermore, prior information of sample, quality of sample subset and subset invariability are together used to check the sample subsets, and the incompatible arrangements of subsets are immediately ruled out in sample check stage, which speeds up the iteration further. Results of the experiment have proved the good performance of the presented PSSC-RANSAC at 90% contamination level. For typical image pairs, the number of iterations is reduced by at least 16.67% and evaluation computation is reduced by at least 11.01% compared with SVH-RANSAC, and the reprojection error is decreased by at least 4.44% and 6.31% compared with RANSAC and SVH-RANSAC, respectively. It can overcome the interferences, and is very suitable for image registration of aerial images.
In order to observe night vision image easily, a new image fusion method is designed to improve the detail information of night vision images in a simple and efficient way. Instead of the traditional Multi-resolution analysis and spatial transform approach, the designed method highlights the detail information of night vision images by phase modulation and image enhancement technique. In the designed approach, the phase spectrum and amplitude spectrum of the visible and infrared images are extracted using FFT firstly, and then the phase spectra of two images are exchanged and the IFFT is applied to the processed images to produce phase information images. To compensate for the blurring caused by phase modulation, the high-frequency information of the processed infrared image is segmented and applied to the reconstruction of the color night vision image. Finally, color night vision image is fused by assigning the two-modulated images to red and green channels respectively, and the segmented image to blue channel. The experimental results show that the details of the fused image by the new method are richer than those of the images fused by the traditional methods, and the designed algorithm with a little amount of calculation can be easily realized in real-time processing systems.
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