Xu-Bo Huang
ABSTRACT
Over the years, with the development of televisions, computers, mobile phones and other equipment, the requirements for clear images, or high-resolution images, have become higher and higher. However, the transmission of high-resolution images is limited by hardware, storage space, and bandwidth, and is not suitable for frequent information interaction. Therefore, if super-resolution reconstruction of low-resolution images can be achieved at the receiving end, the above-mentioned problems can be avoided.
At present, the super-resolution technology is relatively mature and is widely used in aerospace, medical imaging, monitoring equipment and other fields. In this paper, a convolutional neural network is proposed, which will be explained in detail later. The network training uses DIV2K, and the test sets are Set5, Set14 and Urban100. Experimental results show that the network can clearly reconstruct low-resolution images into high-resolution images, and can retain the original information while enlarging. In this sample-structured document, neither the cross-linking of float elements and bibliography nor metadata/copyright information is available. The sample document is provided in "Draft" mode and to view it in the final layout format, applying the required template is essential with some standard steps.
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Index Terms
- An Effective Structure in Single Image Super-resolution
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