Development of a refined illumination and reflectance approach for optimal construction site interior image enhancement
- Publisher:
- Emerald
- Publication Type:
- Journal Article
- Citation:
- Construction Innovation: information, process, management, 2022
- Issue Date:
- 2022-09-08
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Purpose – Images taken from construction site interiors often suffer from low illumination and poor
natural colors, which restrict their application for high-level site management purposes. The state-ofthe-
art low-light image enhancement method provides promising image enhancement results.
However, they generally require a longer execution time to complete the enhancement. This
study aims to develop a refined image enhancement approach to improve execution efficiency and
performance accuracy.
Design/methodology/approach – To develop the refined illumination enhancement algorithm named
enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map.
Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A
coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and
averaging method was used to identify interior site progress.
Findings – The proposed refined approach took around 4.36–4.52 s to achieve the expected results while
outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order
error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity
index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.
Originality/value – The proposed approach provides an alternative to shorten the execution time,
improve equalization of the illumination map and provide a better image reconstruction. The approach could
be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection
processes.
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