In the review, the author says :
where c is [the] number of channels of the image [...]. In this case, c=1 [...]
But the example shows a color image. So, first, I asked myself "How a color image could have only 1 channel ?".
Then I read the following in the paper :
The majority of SR algorithms [...] focus on gray-scale or single-channel image super-resolution. For color images, the aforementioned methods first transform the problem to a different color space (YCbCr or YUV), and SR is applied only on the luminance channel.
Ok, so the network only works with luminance. Therefore how can it generate a color image as an output ? Converting a full color system to only luminance, a part of the information on the color is lost. How does the network get it back in the process ?