## 3 The Challenges Associated with the Application of Shadow Removal Techniques
Application of this method for images with concrete cracks is shown in Figure 7e, and the results are not satisfactory. Shadows are not removed in images 2, 3 and 7; information of concrete texture is lost in images 4-8; and image 1 is almost black. A user-centric technique proposed in [47] is also tested for concrete crack images. The authors of [47] attempt to achieve a fast, robust and high-quality shadow removal by adopting an interactive approach where the user has to provide rough strokes to indicate the shadows and lit image areas. The penumbra is then registered onto a normalized frame in order to estimate the changes of non-uniform shadow illumination, which allows for efficient shadow removal. The results of this technique are shown in Figure 7f and are quite promising. There are still some shadow ghosts in images 2 and 8, as well as some crack features lost in 4 and 5, but overall performance is quite good compared to other techniques. Recent advances in deep learning resulted in new shadow removal methods and techniques. A new solution to shadow removal task has been proposed in [50]. This approach is designed using the shadow-aware FusionNet that takes the shadow image as input to generate fusion weight maps across all the over-exposure images as well as boundary-aware RefineNet to further eliminate the remaining shadow trace. Another approach that also employs deep neural networks is proposed in [51; 52]--the direction-aware attention mechanism in a spatial recurrent neural network (RNN) is formulated by introducing attention weights when aggregating spatial context features in the RNN. This enables the recovery of direction-aware spatial context (DSC) for the detection and removal of shadows. This design is developed into the DSC module and embedded in a convolutional neural network to learn the DSC features at different levels. The recent development of Generative Adversarial Networks (GANs) has also resulted in a promising shadow removal applications such as Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. This framework learns to produce shadow masks and to remove shadows at the same time, which results in increased overall performance. Another example of successful GAN application for shadow removal is based on techniques proposed in [48]. In order to achieve the ghost-free shadow removal, the authors of [48] apply the following techniques: (a) a novel network structure--dual hierarchical aggregation network--is proposed for shadow detection and removal, which hierarchically aggregates multicontext features for attention and prediction; (b) a shadow matting generative adversarial network is designed in order to produce realistic shadows mattings using shadow-free images and shadow masks, and the obtained images are utilized to enhance the training datasets. We tested this method for images with concrete cracks. This technique should enable ghost-free shadow removal; however, the results in Figure 7g show that all images do contain shadow ghosts. The results of images 1-6 are more or less acceptable; however, images 7-8 show that this technique fails completely. All the above examples help to showcase that some shadow removal techniques can be successfully applied to certain images (as demonstrated in the publications addressed above). However, in some cases obtaining a shadow-free image is not robust and the produced images are not suitable for the further crack detection. This difficulty occurs due to the fact that in the concrete images, the shadow shapes are often overlaying the crack. That makes it difficult to differentiate between shadows and cracks and, in turn, to remove shadows without any impact on the cracks. Even with introduction of novel techniques based on artificial neural networks, shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties.