  The aim of blind super-resolution (SR) in computer vision is to improve the
resolution of an image without prior knowledge of the degradation process that
caused the image to be low-resolution. The State of the Art (SOTA) model
Real-ESRGAN has advanced perceptual loss and produced visually compelling
outcomes using more complex degradation models to simulate real-world
degradations. However, there is still room to improve the super-resolved
quality of Real-ESRGAN by implementing recent techniques. This research paper
introduces StarSRGAN, a novel GAN model designed for blind super-resolution
tasks that utilize 5 various architectures. Our model provides new SOTA
performance with roughly 10% better on the MANIQA and AHIQ measures, as
demonstrated by experimental comparisons with Real-ESRGAN. In addition, as a
compact version, StarSRGAN Lite provides approximately 7.5 times faster
reconstruction speed (real-time upsampling from 540p to 4K) but can still keep
nearly 90% of image quality, thereby facilitating the development of a
real-time SR experience for future research. Our codes are released at
https://github.com/kynthesis/StarSRGAN.
