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Automatic detection of complex cracks on rough concrete surfaces via image processing is a challenging task. The most current effective methods involve deep learning schemes. These are usually computationally and structurally complex. Recently, relatively simplified algorithms were developed for effective segmentation of crack features. However, these approaches still could not consistently and accurately extract such features from extremely noisy images of rough concrete surfaces with complex crack patterns. This study describes crack feature segmentation algorithms based on wavelet coefficient adjustment, nonlinear filter pre-processing, saturation channel extraction, adaptive threshold-based edge detection and fuzzy clustering-based area classification. Additional modifications include a new energy function for active contour segmentation algorithm. Adaptive localized mask generation is also proposed for automatic region-based segmentation. Furthermore, a binary fusion stage is incorporated for improved edge feature extraction. The quantitative and visual evaluation of the proposed schemes show improvement in results compared to several recent state-of-the-art algorithms.

In this study, we propose a modified partial differential equation (PDE)-based algorithm for image de-hazing. The algorithm possesses relatively low computational complexity and the core function of the PDE is easily amenable to hardware implementation. New contributions include the optimization and automated processing for dark and hazy images, avoiding manual parameter tuning. Additionally, the regularization parameter is computed adaptively from the binary mask of the input image. This is combined with a gradient-based metric for optimization to automatically determine stopping time of the algorithm for both types of images. The proposed scheme is fast and utilizes spatial or frequency domain filters to achieve illumination and reflectance component estimation without resorting to logarithms. Moreover, there is absence of halos in de-hazed images compared to previous work. Extensive experiments indicate that the proposed approach yields results comparable to or better than several works from the literature.