  Single Image Super Resolution (SISR) is the task of producing a high
resolution (HR) image from a given low-resolution (LR) image. It is a well
researched problem with extensive commercial applications such as digital
camera, video compression, medical imaging and so on. Most super resolution
works focus on the features learning architecture, which can recover the
texture details as close as possible. However, these works suffer from the
following challenges: (1) The low-resolution (LR) training images are
artificially synthesized using HR images with bicubic downsampling, which have
much richer-information than real demosaic-upscaled mobile images. The mismatch
between training and inference mobile data heavily blocks the improvement of
practical super resolution algorithms. (2) These methods cannot effectively
handle the blind distortions during super resolution in practical applications.
In this work, an end-to-end novel framework, including high-to-low network and
low-to-high network, is proposed to solve the above problems with dual
Generative Adversarial Networks (GAN). First, the above mismatch problems are
well explored with the high-to-low network, where clear high-resolution image
and the corresponding realistic low-resolution image pairs can be generated.
Moreover, a large-scale General Mobile Super Resolution Dataset, GMSR, is
proposed, which can be utilized for training or as a fair comparison benchmark
for super resolution methods. Second, an effective low-to-high network (super
resolution network) is proposed in the framework. Benefiting from the GMSR
dataset and novel training strategies, the super resolution model can
effectively handle detail recovery and denoising at the same time.
