  Convolutional Neural Networks (CNNs) have demonstrated great results for the
single-image super-resolution (SISR) problem. Currently, most CNN algorithms
promote deep and computationally expensive models to solve SISR. However, we
propose a novel SISR method that uses relatively less number of computations.
On training, we get group convolutions that have unused connections removed. We
have refined this system specifically for the task at hand by removing
unnecessary modules from original CondenseNet. Further, a reconstruction
network consisting of deconvolutional layers has been used in order to upscale
to high resolution. All these steps significantly reduce the number of
computations required at testing time. Along with this, bicubic upsampled input
is added to the network output for easier learning. Our model is named
SRCondenseNet. We evaluate the method using various benchmark datasets and show
that it performs favourably against the state-of-the-art methods in terms of
both accuracy and number of computations required.
