Deep learning‐based super‐resolution with feature coordinators preservation for vision‐based measurement. Issue 12 (30th September 2022)

• Record Type: Journal Article
• Authors: Wu, Lijun; Wang, Yajin; Lin, Xufeng; Chen, Zhicong; Zheng, Qiao; Cheng, Shuying; Lin, Peijie
• Is Part Of: Structural control and health monitoring. Volume 29:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-30
• Subjects: deep learning -- feature preserving -- image degradation model -- super‐resolution -- visual measurement; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.3107
• Languages: English
• ISSNs: 1545-2255

Abstract

Abstract: Vision‐based displacement measurement is promising and emerging for structural monitoring. However, the accuracy of visual measurement is commonly limited by the resolution of the camera. The super‐resolution (SR) technique can reconstruct high‐resolution images from the corresponding low‐resolution images within the constraints of prior knowledge. Existing SR algorithms mainly focus on improving the overall quality of the image. By contrast, the accurate extraction of the coordinates of feature points is the most important for the visual measurement. Besides, the SR network is usually trained by an artificial dataset whose low‐resolution images are obtained by artificially degrading the corresponding high‐resolution images, instead of those directly captured by cameras. However, this degradation usually is only a simple bicubic downsampling that cannot reflect the real degradation, which will provide inaccurate constraints to the network training. Therefore, this paper proposes a novel SR framework that can significantly preserve the feature coordinators for visual measurement (SRFCP). First, a deep learning‐based SR network that focuses on feature preservation is proposed, which introduces both feature weighted branch and feature preserving loss. Second, an image degradation model is built based on the blur kernel and noise extracted from the images captured in real scene. Experiments on public datasets show that the proposed SRFCP performs well both in terms of the objective evaluation index and the subjective visual effect. Then, a binocular visual measurement platform is set up to measure the distance of adjacent feature points on a chessboard. Lastly, several SR algorithms are evaluated by the improvement they bring to the measurement accuracy. Experimental results show that the distance measurement performance can be significantly improved by the images reconstructed by the SRFCP. Therefore, the proposed SRFCP can accurately reconstruct the high‐resolution images while preserving the features coordinates, which is crucial for the visual measurement in structural monitoring.