Cascaded Deep Networks with Multiple Receptive Fields for Infrared Image Super-Resolution

Zewei He, Siliang Tang, Jiangxin Yang, Yanlong Cao (Corresponding Author), Michael Ying Yang, Yanpeng Cao

Research output: Contribution to journalArticleAcademicpeer-review

59 Citations (Scopus)
176 Downloads (Pure)


Infrared images have a wide range of military and civilian applications including night vision, surveillance and robotics. However, high-resolution infrared detectors are difficult to fabricate and their manufacturing cost is expensive. In this work, we present a cascaded architecture of deep neural networks with multiple receptive fields to increase the spatial resolution of infrared images by a large scale factor (×8). Instead of reconstructing a high-resolution image from its low-resolution version using a single complex deep network, the key idea of our approach is to set up a mid-point (scale ×2) between scale ×1 and ×8 such that lost information can be divided into two components. Lost information within each component contains similar patterns thus can be more accurately recovered even using a simpler deep network. In our proposed cascaded architecture, two consecutive deep networks with different receptive fields are jointly trained through a multi-scale loss function. The first network with a large receptive field is applied to recover large-scale structure information, while the second one uses a relatively smaller receptive field to reconstruct small-scale image details. Our proposed method is systematically evaluated using realistic infrared images. Compared with state-of-theart Super-Resolution methods, our proposed cascaded approach achieves improved reconstruction accuracy using significantly less parameters.
Original languageEnglish
Article number8432397
Pages (from-to)2310-2322
Number of pages13
JournalIEEE transactions on circuits and systems for video technology
Issue number8
Publication statusPublished - 10 Aug 2018




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