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

Karam Park, Nam Ik Cho; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2653-2663

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.