GPU-CPU implementation for super-resolution mosaicking of Unmanned Aircraft System (UAS) surveillance video

Unmanned Aircraft Systems (UAS) have been used in many military and civilian applications, particularly surveillance. One of the best ways to use the capacity of a UAS imaging system is by constructing a mosaic of the recorded video. In this paper, we present a novel algorithm to calculate a super-resolution mosaic for UAS, which is both fast and robust. In this algorithm, the features points between frames are found using SIFT (Scale-Invariant Feature Transform), and then RANSAC (Random Sample Consensus) is used to estimate the homography between two consecutive frames. Next, a low-resolution (LR) mosaic is computed. LR images are extracted from the LR mosaic, and then they are subtracted from the input frames to form LR error images. These images are used to compute an error mosaic. The regularization technique uses Huber prior information and is added to the error mosaic to form the superresolution (SR) mosaic. The proposed algorithm was implemented using both a GPU (Graphics Processing Unit) and a CPU (Central Processing Unit). The first part of the algorithm, which is the construction of the LR mosaic, is performed by the GPU, and the rest is performed by the CPU. As a result, there is a significant speed-up of the algorithm. The proposed algorithm has been tested in both the infrared (IR) and visible spectra, using real and synthetic data. The results for all these cases show a great improvement in resolution, with a PSNR of 41.10 dB for synthetic data, and greater visual detail for the real UAV surveillance data.