Abstract
An efficient network for super-resolution, which we refer to as inception learning super-resolution (ILSR), is proposed. We adopt the inception module from GoogLeNet to exploit multiple features from low-resolution images, yet maintain fast training steps. The proposed ILSR network demonstrates low computation time and fast convergence during the training process. It is divided into three parts: feature extraction, mapping, and reconstruction. In feature extraction, we apply the inception module followed by dimensional reduction. Then, we map features using a simple convolutional layer. Finally, we reconstruct the high-resolution component using the inception module and a convolutional layer. Experimental results demonstrate that the proposed network can construct sharp edges and clean textures, and reduce computation time by up to three orders of magnitude compared to state-of-the-art methods.
© 2017 Optical Society of America
Full Article | PDF ArticleMore Like This
Xiuwei Yang, Dehai Zhang, Zhongmin Wang, Yanbo Zhang, Jun Wu, Biyuan Wu, and Xiaohu Wu
Appl. Opt. 61(12) 3363-3370 (2022)
Aniwat Juhong, Bo Li, Cheng-You Yao, Chia-Wei Yang, Dalen W. Agnew, Yu Leo Lei, Xuefei Huang, Wibool Piyawattanametha, and Zhen Qiu
Biomed. Opt. Express 14(1) 18-36 (2023)
Zhenyu Long, Tianyi Wang, ChengWu You, Zhengang Yang, Kejia Wang, and Jinsong Liu
Appl. Opt. 58(10) 2731-2735 (2019)