DCT and PCA based method for shape from focus

Abstract

Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) are widely used in computer vision applications. In this paper, we introduce a new SFF method based on DCT and PCA. Contrary to computing focus quality locally by summing all values in a 2D or 3D window obtained after applying a focus measure, a vector consisting of seven neighboring pixels is populated for each pixel in the image volume. PCA is applied on the AC part of the DCT of each vector in the sequence to transform data into eigenspace. Considering the first feature, as it contains maximum variation, and discarding all others, is employed to compute the depth. Though DCT and PCA are both computationally expensive transformations, the reduction in data elements and algorithm iterations have made the new approach efficient. Experimental results are presented to demonstrate the effectiveness of new method by using three different image sequences. © 2008 Springer-Verlag Berlin Heidelberg.