Dynamic Depth-Aware Network for Endoscopy Super-Resolution
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 5189-5200 |
Number of pages | 12 |
Journal / Publication | IEEE Journal of Biomedical and Health Informatics |
Volume | 26 |
Issue number | 10 |
Online published | 6 Jul 2022 |
Publication status | Published - Oct 2022 |
Link(s)
Abstract
Endoscopy super-resolution (SR) plays an
important role in improving diagnostic results and reducing
the misdiagnosis rate. Even though recent studies have
investigated the SR for endoscopy, these methods apply
equal importance to the whole image and do not
consider the relationship among pixels, especially the
depth information, which can provide diagnosis-related
information for clinicians. To address this problem, we
propose a dynamic depth-aware network for endoscopy
super-resolution, which represents the first effort to
comprehensively integrate the depth information to the
SR task for endoscopic images. It includes a depth-wise feature extracting branch (DW-B) and a depth-guided
SR branch (DGSR-B). The DW-B aims to extract the
representative feature for each depth level (i.e. depth
matrix) further to provide auxiliary information and guide
the super-resolution of texture under different depth levels.
In DGSR-B, a depth-guided block (DGB) consisting of
depth-focus normalization (DFN) is introduced to inject
both the depth matrix and depth map into the LR image
feature, so as to guide the image generation for each
depth region. To adaptively super-resolve the regions
under different depth levels, we devise a dynamic depth-aware loss to assign different trainable weights to each
region for SR optimization. Extensive experiments have
been conducted on two main publicly available datasets,
i.e., the Kvasir dataset and the EndoScene dataset, and
the superior performance verifies the effectiveness of our
method for SR task and polyp segmentation. Source code
is to be released.
Research Area(s)
- Cancer, Deep learning, depth information, Endoscopes, Endoscopy, Feature extraction, polyp segmentation, super-resolution, Superresolution, Task analysis, Three-dimensional displays
Citation Format(s)
Dynamic Depth-Aware Network for Endoscopy Super-Resolution. / Chen, Wenting; Liu, Yifan; Hu, Jiancong et al.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 10, 10.2022, p. 5189-5200.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 10, 10.2022, p. 5189-5200.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review