Inception learning super-resolution

Muhammad Haris, Muhammad Rahmat Widyanto, Hajime Nobuhara

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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 1 × 1 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.

Original languageEnglish
Pages (from-to)6043-6048
Number of pages6
JournalApplied Optics
Issue number22
Publication statusPublished - 1 Aug 2017


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