Abstract
Image super-resolution has always been a research hotspot in the field of computer vision. Recently, image super-resolution algorithms using convolutional neural networks (CNN) have achieved good performance. But the existing methods based on CNN usually has to many (20–30) convolution layers, which has a large amount of calculations. In response to this problem, this paper proposes a lightweight network model based on parallel convolution, skip connections and ResNet. Parallel convolution means that different sizes of convolution kernels are set in the same convolutional layer to extract image features of different scales. In addition, in order to reduce the loss of image details, we combine the input and output of the previous layer as the input of the next layer, which is the skip connection. We also borrowed the idea of Residual Net. The network learns the residuals between high-resolution images and low-resolution images. Therefore, the algorithm proposed in this paper not only achieves the most advanced performance, but also achieves faster calculations.
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Acknowledgements
This work was supported by the Natural Science Foundation of China (No. 61502283). Natural Science Foundation of China (No. 61472231). Natural Science Foundation of China (No. 61640201).
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Wang, Q., Qi, F. (2019). Single Image Super-Resolution by Parallel CNN with Skip Connections and ResNet. In: Tang, Y., Zu, Q., Rodríguez García, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_18
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