###### Abstract
Only limited works are solely dedicated and reported for vision-based SHM in a dark or night environment using real images. [PERSON] et al. [18] conducted a dynamic test using a smartphone and [PERSON] et al. [19] installed a vision-based monitoring system equipped with a digital camera with a zoom lens on a three-span cable-stayed bridge. However, these two studies were conducted in low light and completely dark settings without additional lighting, so the SHM was unable to identify the monitored object [19] and a significant quantity of time-signals were missing in the data [18]. To solve these problems, a small number of studies that added additional components to the vision-based system have been reported. An SHM using a smartphone camera with a laser device was reported by [PERSON] et al. [20]. [PERSON] et al. [21] proposed a night vision camera equipped with an IR pass filter to remove the red-eye effect in the infrared region. Digital cameras with LED lights as targets were used for night monitoring as evaluated by [PERSON] et al. [22]. In terms of accuracy, these studies showed good precision and promising results; however, they only validated their works using low-amplitude testing. Post-processing underexposed images using image enhancement algorithms is also a solution as it improves the image quality. Histogram equalization [23,24] was used to enhance image gray resolution for crack detection [25] and crack monitoring from thermal imaging [26]. Wavelet transforms [27,28] were used to correct vision-based images for damage and crack detection [29,30] and fatigue crack detection [31]. Contrast enhancement was conducted on vision-based images to separate the crack and the background area [32], and the advanced deep learning method was capable of autonomously detecting concrete cracking, steel corrosion, and delamination [33]. A recent study by [PERSON] et al. [34] deployed UAV monitoring and applied a contrast enhancement technique on imaging photogrammetry to enable monitoring on the deteriorated concrete area. Image enhancement is also commonly integrated into other remote sensing fields such as satellite imagery [35] and aerial system imagery [36]. However, at the present time, almost no relevant works can be referred to in this study that are related to vision-based system image enhancement with specific implementation for vibration SHM purposes. Although prior studies successfully conducted SHM under low-light settings and night environments with good accuracy and by integrating image enhancement methods, several research gaps can still be identified. First, an alternative SHM framework can be proposed to improve the real vision-based SHM data under the complexity of a dark environment without assistance from additional equipment or hardware. Second, a specific study of vibration-based SHM in a dark environment should be conducted because available studies were only proposed for the damage-detection SHM. Third, more experiments are necessary to identify SHM accuracy, ranging from a very small displacement to higher amplitudes, as the prior works only validated their SHM framework under a very low amplitude of dynamic excitation. To fill these gaps, this study proposes the integration of image enhancement algorithms for low-light settings and dark environments. The objective of this study is to modify the underexposed and low-contrast image characteristics to improve their quality before implementation into automatic processing of bundle adjustment in close-range photogrammetry. The goal is to assess the accuracy of the enhanced images in measuring displacement and in identifying structural dynamic properties through system identification.