  In a structural health monitoring (SHM) system that uses digital cameras to
monitor cracks of structural surfaces, techniques for reliable and effective
data compression are essential to ensure a stable and energy efficient crack
images transmission in wireless devices, e.g., drones and robots with high
definition cameras installed. Compressive sensing (CS) is a signal processing
technique that allows accurate recovery of a signal from a sampling rate much
smaller than the limitation of the Nyquist sampling theorem. The conventional
CS method is based on the principle that, through a regularized optimization,
the sparsity property of the original signals in some domain can be exploited
to get the exact reconstruction with a high probability. However, the strong
assumption of the signals being highly sparse in an invertible space is
relatively hard for real crack images. In this paper, we present a new approach
of CS that replaces the sparsity regularization with a generative model that is
able to effectively capture a low dimension representation of targeted images.
We develop a recovery framework for automatic crack segmentation of compressed
crack images based on this new CS method and demonstrate the remarkable
performance of the method taking advantage of the strong capability of
generative models to capture the necessary features required in the crack
segmentation task even the backgrounds of the generated images are not well
reconstructed. The superior performance of our recovery framework is
illustrated by comparing with three existing CS algorithms. Furthermore, we
show that our framework is extensible to other common problems in automatic
crack segmentation, such as defect recovery from motion blurring and occlusion.
