  In current practice, scene survey is carried out by workers using total
stations. The method has high accuracy, but it incurs high costs if continuous
monitoring is needed. Techniques based on photogrammetry, with the relatively
cheaper digital cameras, have gained wide applications in many fields. Besides
point measurement, photogrammetry can also create a three-dimensional (3D)
model of the scene. Accurate 3D model reconstruction depends on high quality
images. Degraded images will result in large errors in the reconstructed 3D
model. In this paper, we propose a method that can be used to improve the
visibility of the images, and eventually reduce the errors of the 3D scene
model. The idea is inspired by image dehazing. Each original image is first
transformed into multiple exposure images by means of gamma-correction
operations and adaptive histogram equalization. The transformed images are
analyzed by the computation of the local binary patterns. The image is then
enhanced, with each pixel generated from the set of transformed image pixels
weighted by a function of the local pattern feature and image saturation.
Performance evaluation has been performed on benchmark image dehazing datasets.
Experimentations have been carried out on outdoor and indoor surveys. Our
analysis finds that the method works on different types of degradation that
exist in both outdoor and indoor images. When fed into the photogrammetry
software, the enhanced images can reconstruct 3D scene models with
sub-millimeter mean errors.
