A novel strategy of multi-scale conditional super-resolution

Image super-resolution is an ill-posed problem, which means that one low-resolution image may correspond to multiple different high-resolution images. We proposed a novel strategy of non-blind multi-scale conditional super-resolution (MSCSR), which not only uses low-resolution image but also uses label of scale factor as the model input, and trains the network using image with the corresponding scale factor. To embed the condition into the network is to combine the condition label with the channel attention. Our model can reconstruct images with different degrees of information by inputting the same low-resolution image and different scale factors.