Slice-Based Super-Resolution Using Light-Weight Network With Relation Loss

While the performance of convolutional neural networks (CNNs)-based single image super-resolution (SISR) has been greatly improved, the enormous parameter sizes and computational complexity of the underlying CNNs make hardware implementation difficult. Recently, several lightweight SISR methods have been developed, but they still do not consider various structural problems that may occur in hardware implementation. To solve this problem, we propose a slice-based SR using light-weight network (LWN) and a slice-based SR using LWN with relation loss (LWNRL). First, LWN(RL) adopts a slice-based architecture to facilitate system-on-chip (SoC) implementation. Second, LWN(RL) avoids global connection modules that are not suitable for SoC implementation, with minimal performance penalty. Finally, we propose a new loss to improve the performance of LWN without additional cost. Experimental results show that LWNRL achieves significant efficiency of SR model. Especially, the larger the resolution or scale factor, the better the performance of LWNRL than the conventional methods.