Really? Do you care to explain? What is the dividend and what is the divisor? Why can dividing a image by its low pass filtered version (or vice versa) be used to "clean up" the image, i.e. subtract the background, find main colors and cluster similar colors with k-means? What if the divisor has pixels near zero?
Areas of low contrast become whiter and areas of high contrast become more saturated.
It is also more robust than k-means. The author's algo will only work on scanned images. Photographed pages from a book will often have a slight shadow on half the page from the curvature. Blur-divide will clean this up. K-means will think you've used a lot of gray and not figure out that there are multiple background colors.
I can confirm that the author's approach doesn't work well for photographed pages. I took a photograph[0] of a page of notes, and due to the shadow, the results[1] were very unsatisfactory.
