  Single image super-resolution (SISR) is a very popular topic nowadays, which
has both research value and practical value. In daily life, we crop a large
image into sub-images to do super-resolution and then merge them together.
Although convolution neural network performs very well in the research field,
if we use it to do super-resolution, we can easily observe cutting lines from
merged pictures. To address these problems, in this paper, we propose a refined
architecture of SRCNN with 'Symmetric padding', 'Random learning' and 'Residual
learning'. Moreover, we have done a lot of experiments to prove our model
performs best among a lot of the state-of-art methods.
