We've only looked at reducing the depth of field as the narrow aperture on smartphone cameras leads to an extended depth of field (meaning that everything is usually in focus within the frame).
We're primarily targeting providing depth of field adjustment tools for video as having multiple frames of data to work with obviously makes the task easier (although we can still get great results with a single image).
We're also experimenting with other filters, not just Gaussian - it's the depth extraction part that we've been trying to innovate on.
Google's approach requires multiple images to be captured of the same subject from different angles in order to triangulate depth - our SIFT flow based approach can achieve similar performance using only a single image. This makes it as simple as capturing a normal image to capture one with an editable depth of field.
One primary issue that we've seen with using smartphones as a primary camera is that you're forced to always shoot with a large depth of field, which means that you miss out on achieving that sought after bokeh for your close up or portrait shots.
I've actually been working with a team of former post-doc researchers who've been using SIFT flow techniques to estimate depth information in an image and apply filters to emulate different depths of field - effectively allowing for an image captured with a smartphone to look like it was shot with a different lens (see https://www.dropbox.com/s/7qhfgnwl08vtk63/compare.png?dl=0). We've got some pretty cool demos if anyone's interested.
I remember how satisfying it was to get that demo to work back in 2011 (thanks to the help of Dave Emett). Feels like an incredibly long time ago now! I wonder what framerate people will be able to achieve...
DoF Adjustment: https://www.dropbox.com/s/7qhfgnwl08vtk63/compare.png?dl=0