Low autocorrelation binary sequence for coded exposure imaging

Abstract

Finding a good binary sequence is critical in determining the performance of the coded exposure imaging. Previous methods mostly rely on random search based approach for finding the binary codes, which often fail to find good long sequences due to the exponentially growing search space. In this paper, we present new computationally efficient algorithms for generating the binary sequences, which is especially well suited for both short and long binary sequences. We show that the concept of the low autocorrelation binary sequence that has been well exploited in the information theory community can be applied for generating the fluttering patterns of the shutter is specifically, we present a new measure of a good binary sequence. And we propose two new algorithms: the first algorithm is the modified Legendre sequence based on theoretical approach and the second algorithm is searching based the memetic algorithm. The two proposed algorithms are complementary according to sequence sizes. For short size, the memetic search has the advantage, and the modified Legendre sequence is useful for generating long size sequence. Experiments using both synthetic and real data show that our new algorithms consistently generates better binary sequences for the coded exposure problem, yielding better deblurring and resolution enhancement results compared to the previous methods for generating the binary codes.