  We propose a novel architecture that learns an end-to-end mapping function to
improve the spatial resolution of the input natural images. The model is unique
in forming a nonlinear combination of three traditional interpolation
techniques using the convolutional neural network. Another proposed
architecture uses a skip connection with nearest neighbor interpolation,
achieving almost similar results. The architectures have been carefully
designed to ensure that the reconstructed images lie precisely in the manifold
of high-resolution images, thereby preserving the high-frequency components
with fine details. We have compared with the state of the art and recent deep
learning based natural image super-resolution techniques and found that our
methods are able to preserve the sharp details in the image, while also
obtaining comparable or better PSNR than them. Since our methods use only
traditional interpolations and a shallow CNN with less number of smaller
filters, the computational cost is kept low. We have reported the results of
two proposed architectures on five standard datasets, for an upscale factor of
2. Our methods generalize well in most cases, which is evident from the better
results obtained with increasingly complex datasets. For 4-times upscaling, we
have designed similar architectures for comparing with other methods.
