Focus stacking, or all-in-focus imaging, is a technique for achieving larger depth of field in an image by fusing images acquired at different focusing distances. Minimizing the set of images to fuse, while ensuring that the resulting fused image is all-in-focus, is important in order to avoid long image acquisition and post-processing times. Recently, an end-to-end system for focus stacking has been proposed that automatically selects images to acquire. The system is adaptive to the scene being imaged and shows excellent performance on a mobile device, where the lens has a short focal length and fixed aperture, and few images need to be selected. However, with longer focal lengths, variable apertures, and more selected images (as exists with other cameras, notably DSLRs), classification and algorithmic inaccuracies become apparent. In this paper, we propose improvements to previous work that remove these limitations, and show on eight real scenes that overall our techniques lead to improved accuracy while reducing the number of required images.