Separating the direct and global components of a single image

Direct and global component separation is an approach to study the light transport that provides a basic understanding of the property of a scene. The conventional technique for separation relies on multiple images or an approximation which results in loss of spatial resolution. In this article, we propose a novel single image separation technique by introducing a linear basis equation with full resolution. We evaluate the data independent Fourier basis and learning-based PCA basis to locate the better basis representation of direct and global components. We carefully analyze the importance of high spatial frequency pattern to the effectiveness of our technique. Moreover, we propose the performance enhancement technique to reduce memory usage and computation time for practical implementation. The experimental results confirm that our proposed method delivers higher separation accuracy and better image quality than the previous methods and is applicable to challenging video sequences.