An Accurate and Lightweight Method for Human Body Image Super-Resolution
In this paper, we propose a new method to super-resolve low resolution human body images by learning efficient multi-scale features and exploiting useful human body prior. Specifically, we propose a lightweight multi-scale block (LMSB) as basic module of a coherent framework, which contains an image...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 01., Seite 2888-2897 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In this paper, we propose a new method to super-resolve low resolution human body images by learning efficient multi-scale features and exploiting useful human body prior. Specifically, we propose a lightweight multi-scale block (LMSB) as basic module of a coherent framework, which contains an image reconstruction branch and a prior estimation branch. In the image reconstruction branch, the LMSB aggregates features of multiple receptive fields so as to gather rich context information for low-to-high resolution mapping. In the prior estimation branch, we adopt the human parsing maps and nonsubsampled shearlet transform (NSST) sub-bands to represent the human body prior, which is expected to enhance the details of reconstructed human body images. When evaluated on the newly collected HumanSR dataset, our method outperforms state-of-the-art image super-resolution methods with ∼ 8× fewer parameters; moreover, our method significantly improves the performance of human image analysis tasks (e.g. human parsing and pose estimation) for low-resolution inputs |
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| Beschreibung: | Date Completed 23.07.2021 Date Revised 23.07.2021 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2021.3055737 |