An improved EnlightenGAN shadow removal framework for images of cracked concrete

Abstract

The concrete crack images in engineering are usually obscured by unexpected shadow and their removal is usually required which is always a challenging task due to its complexity and diversity. An improved Enlighten Generative Adversarial Networks (IEnlightenGAN) is developed in a proposed framework to improve shadow removal results. The Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) is firstly developed to improve the concrete image resolution. Residual-in-residual-dense blocks module and Atrous Spatial Pyramid Pooling module are then integrated into the IEnlightenGAN to improve its shadow removal capability. A quality assessment methodology on the shadow removal results is also proposed. The crack contour is extracted based on U-Net, and denoising is conducted based on morphological closing. Image segmentation metrics are used for the evaluation. The proposed IEnlightenGAN and the evaluation method are trained and verified using a dataset containing both normal and shadowed concrete crack images with comparison of results from the state-of-the-art models. The proposed shadow removal framework is found able to improve the quality of shadowed crack images, and the proposed quality assessment method is also found better than existing approach.