Video Super-Resolution Using a Grouped Residual in Residual Network
MohammadHossein Ashoori Arash Amini
Super-resolution (SR) is the technique of increasing the nominal resolution
of image / video content accompanied with quality improvement. Video
super-resolution (VSR) can be considered as the generalization of single image
super-resolution (SISR). This generalization should be such that more detail is
created in the output using adjacent input frames. In this paper, we propose a
grouped residual in residual network (GRRN) for VSR. By adjusting the
hyperparameters of the proposed structure, we train three networks with
different numbers of parameters and compare their quantitative and qualitative
results with the existing methods. Although based on some quantitative
criteria, GRRN does not provide better results than the existing methods, in
terms of the quality of the output image it has acceptable performance.