Abstract
Recently, Convolutional Neural Networks (CNN) have made an impressive breakthrough in single image super-resolution (SISR). By utilizing the advantages of densely connected neural network and classical residual network, we propose a deep convolutional network named residual restructured dense network (RRDN). By combining dense concatenation and residual skip connection, we design residual restructured dense block (RRDBlock), which extracts long and short memory features with different receptive fields and helps the network reuse effective features. Moreover, direct paths from the previous layers to the subsequent layers helps RRDN mitigate the problems of gradient vanishing and instability during training. We evaluate the proposed method using four standard benchmark datasets and the results show that RRDN achieves high reconstruction performance.
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Wang, Y., Rong, Y., Zheng, H., Liu, A. (2019). Super Resolution via Residual Restructured Dense Network. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_68
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DOI: https://doi.org/10.1007/978-3-030-31723-2_68
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