Partial Filter-Sharing: Improved Parameter-sharing Method for Single Image Super-Resolution Networks

Numerous deep learning techniques have been developed for Single Image Super-Resolution (SISR), leading to significant performance improvements. However, these techniques have also resulted in a substantial increase in parameter size. As a result, there is a growing interest in reducing network complexity for more practical usage while still maintaining high SR quality. One such method is parameter-sharing, which includes recursive, recurrent, and multi-scale learning approaches. However, sharing identical kernels across layers or up-scaling tasks can reduce the network's representational capacity. To address this, we propose Partial filter-Sharing (PS), a new parameter-sharing method that preserves the network's representational power more effectively than previous approaches. Instead of sharing a single filter, PS shares segments of filters, called partial filters, across layers. This approach enables parameter-sharing layers to use diverse filters for each layer or task, striking a balance between parameter efficiency and the network's representational ability without imposing excessive computational or parameter overhead. Furthermore, the PS framework provides precise control over the network's performance and complexity by adjusting the quantity of partial filters. Extensive experiments demonstrate that our PS framework outperforms traditional parameter-sharing super-resolution (SR) methods without incurring excessive additional parameters or computational cost. Our code is available here: https://github.com/saturnian77/Partial_filter-Sharing.