Abstract
With the development of deep learning (DL), convolutional neural networks (CNNs) have shown great reconstruction performance in single image super-resolution (SISR). However, some methods blindly deepen the networks to purchase the performance, which neglect to make full use of the multi-scale information of different receptive fields and ignore the efficiency in practice. In this paper, a lightweight SISR network with multi-scale information fusion blocks (MIFB) is proposed to fully extract information via a multiple ranges of receptive fields. The features are refined in a coarse-to-fine manner within each block. Group convolutional layers are employed in each block to reduce the number of parameters and operations. Results of extensive experiments on the benchmarks show that our method achieves better performance than the state-of-the-arts with comparable parameters and multiply–accumulate (MAC) operations.
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