Abstract
Densely connected network for Image Super-Resolution (SR) has achieved much better results than most of the other methods owing to its dense connection architecture which can provide more and deeper features for image super-resolution. However, since the dense block accepts the outputs of all previous blocks, it receives a lot of redundant and conflicting information, which results in longer training time and bad super-resolution reconstruction results. To solve this problem, we introduce an attention module into a densely connected network and propose an attention-based densely connected network (ADSRNet) for image super-resolution. With the attention module, our ADSRNet can select more important information and cut off those redundant for image super-resolution from a large number of feature maps by importance ordering. Thus, we can speed up the training of network. Extensive experiments are performed over the datasets Set5, Set14 and BSD100, the qualitatively and quantitatively evaluated results for our proposed ADSRNet are better than ones of some state-of-the-art methods.
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Acknowledgemet
This work is supported by the National Natural Science Foundation of China (No.61273273), by the national Key Research and Development Plan (No.2017YFC0112001).
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Li, W., Lu, Y., Wang, X., Chen, X., Wang, Z. (2019). ADSRNet: Attention-Based Densely Connected Network for Image Super-Resolution. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_23
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DOI: https://doi.org/10.1007/978-3-030-31726-3_23
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