KEYWORDS: Image segmentation, Image restoration, Cameras, Point spread functions, Filtering (signal processing), Parallel computing, Image processing algorithms and systems, Modulation transfer functions, Sun, Signal to noise ratio
To eliminate side-oblique image motion, a fast image algorithm is proposed for implementation on aerial camera systems. When an aerial camera works at a side-oblique angle, much parallel image motion with different rates will occur on the focal plane array simultaneously. Through analysis of how different rates of parallel image motion blur are generated and the relationship between image motion and the field of view (FOV) angle in side-oblique situations, the entire blurred image can be segmented into many slices by their different rates of image motion. To be computed quickly, the slices are divided into pixel lines continuously, and then a specific parallel computing scheme is presented using 1-D Wiener filters to restore all the pixel lines. With all the resulting pixel lines combined, the restoration image comes into being. The experiment results show that the proposed algorithm can effectively restore the details of side-oblique blurred images. The peak signal-to-noise ratio (PSNR) of the restored image can reach 31.426. With the help of the parallel computing capability of a graphics processing unit (GPU), the proposed algorithm can restore a 2048×2048 8-bit blurred image in 17 ms, realizing real-time restoration.