  The current existing deep image super-resolution methods usually assume that
a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR)
image. However, such an ideal bicubic downsampling process is different from
the real LR degradations, which usually come from complicated combinations of
different degradation processes, such as camera blur, sensor noise, sharpening
artifacts, JPEG compression, and further image editing, and several times image
transmission over the internet and unpredictable noises. It leads to the highly
ill-posed nature of the inverse upscaling problem. To address these issues, we
propose a GAN-based SR approach with learnable adaptive sinusoidal
nonlinearities incorporated in LR and SR models by directly learn degradation
distributions and then synthesize paired LR/HR training data to train the
generalized SR model to real image degradations. We demonstrate the
effectiveness of our proposed approach in quantitative and qualitative
experiments.
