Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
This work addresses efficiency issues in image super-resolution for applications requiring lightweight models, though it appears incremental as it builds on existing recursive methods.
The paper tackles the problem of large model sizes and high computational complexity in deep learning-based image super-resolution by proposing a lightweight and efficient method using a block state-based recursive network, achieving state-of-the-art performance with reduced model size and improved speed.
Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers. However, this leads too large model sizes and high computational complexities, thus some recursive parameter-sharing methods have been also proposed. Nevertheless, their designs do not properly utilize the potential of the recursive operation. In this paper, we propose a novel, lightweight, and efficient super-resolution method to maximize the usefulness of the recursive architecture, by introducing block state-based recursive network. By taking advantage of utilizing the block state, the recursive part of our model can easily track the status of the current image features. We show the benefits of the proposed method in terms of model size, speed, and efficiency. In addition, we show that our method outperforms the other state-of-the-art methods.