Fast binary image resolution increasing by k-nearest neighbor learning

In a typical office environment, heterogeneous devices and software, each working in a different spatial resolution, must interact together. Thus, there frequently arises the resolution conversion problem. The present paper addresses the spatial resolution increasing of binary images and documents (for example, conversion of a 300 dpi image into 600 dpi). A new and efficient solution to this problem is proposed. It makes use of k-nearest neighbor learning to automatically design the optimal windowed zoom operator starting from pairs of in-out sample images. The k-nearest neighbor learning has good inductive bias that allows reducing the quantity of the training sample images needed. The resulting operator is stored in a look-up-table, which is extremely fast computationally.