Abstract
Recent years have witnessed remarkable progress in convolutional neural network (CNN) based image super-solution (SR) methods. Existing methods tend to deepen the network by means of residual skip connections to achieve better performance. However, these methods are still hard to be applied in real-world applications due to the requirement of its heavy computation. In this paper, we propose a Deep Feature Recalibration Network (DFRN), which strives for efficiency yet effective networks. We divide the process of network nonlinear mapping into two steps: information integration and feature enhancement, and proposed two types of block models: Multi-Scale Information Integration Block (MSIIB) and Feature Recalibration Block (FRB). MSIIB integrates the representation of the input data in the network with different size of receptive fields. FRB enhances the information via obtaining the attention along two different dimensions (channel and plane space of feature maps) respectively. By combining MSIIB and FRB, we provide a more efficient and time-saving method for SISR. Experiments show that the proposed DFRN method outperforms state-of-the-art methods in terms of both objective evaluation metrics (PSNR, SSIM, and running speed) and subjective perception on the generated images.
Supported in part by the National Key Research and Development Program of China under Grant 2018AAA0103202, in part by the National Natural Science Foundation of China under Grant 61922066, Grant 61876142, Grant 61671339, Grant 61772402, Grant U1605252, and Grant 61976166, in part by the National High-Level Talents Special Support Program of China under Grant CS31117200001, in part by the Fundamental Research Funds for the Central Universities under Grant JB190117, in part by the Xidian University Intellifusion Joint Innovation Laboratory of Artificial Intelligence, and in part by the Innovation Fund of Xidian University.
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Xin, J., Jiang, X., Wang, N., Li, J., Gao, X. (2020). Image Super-Resolution via Deep Feature Recalibration Network. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_21
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