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In recent years, deep-based models have achieved great success in the field of single image super-resolution (SISR), where tremendous parameters are always needed to obtain a satisfying performance. However, the high computational complexity extremely limits its applications to some mobile devices that possess less computing and storage resources. To address this problem, in this paper, we propose a flexibly adjustable super lightweight SR network: s-LWSR. Firstly, in order to efficiently abstract features from the low resolution image, we design a high-efficient U-shape based block, where an information pool is constructed to mix multi-level information from the first half part of the pipeline. Secondly, a compression mechanism based on depth-wise separable convolution is employed to further reduce the numbers of parameters with negligible performance degradation. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information, thus leading to the final performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance compared with other cumbersome DL-SR methods.
Li et al. (Wed,) studied this question.