  Divide and conquer is an established algorithm design paradigm that has
proven itself to solve a variety of problems efficiently. However, it is yet to
be fully explored in solving problems with a neural network, particularly the
problem of image super-resolution. In this work, we propose an approach to
divide the problem of image super-resolution into multiple sub-problems and
then solve/conquer them with the help of a neural network. Unlike a typical
deep neural network, we design an alternate network architecture that is much
wider (along with being deeper) than existing networks and is specially
designed to implement the divide-and-conquer design paradigm with a neural
network. Additionally, a technique to calibrate the intensities of feature map
pixels is being introduced. Extensive experimentation on five datasets reveals
that our approach towards the problem and the proposed architecture generate
better and sharper results than current state-of-the-art methods.
