## 3 The Challenges Associated with the Application of Shadow Removal Techniques  
As mentioned earlier, using shadow removal methods before the application of deep-learning techniques as a means to improve the classification of concrete images is not straightforward. The main difficulty of such approach is related to the fact that shadow removal can often cause serious deterioration of the image quality. The original concrete images with and without cracks are shown in Figure 7. All images are captured in the presence of different shadow pattern. Firstly, a methodology proposed in [34] is applied to two concrete images from the testing dataset described earlier. The main idea of this technique is as follows: an illumination invariant shadow-free image is derived and utilized to detect the edges of the shadow, which are then used to remove the shadow from the original image. The results of this procedure are displayed in the Figure 7b. It can be seen that the quality of the obtained images is severely compromised. Further image processing related to the detection and/or identification of concrete cracks on such an image could be very challenging. The results are not that bad in images 2, 3 and 7; however, a method completely fails with ghost-free shadow removal in images 1, 4-6, and 8. A similar approach--the method of shadow detection and removal using mean and Gaussian filtering outlined in [34; 36]--is applied with the results depicted in Figure 7c. Again, it can be seen that the quality of the produced images is very poor and similar to Figure 7b. Furthermore, on one of obtained images the concrete crack is barely visible which would doubtlessly have a negative impact on the accuracy of the concrete image classification methods. Another color-model based shadow detection and removal technique, presented in [44], is applied as follows. Original RGB images are converted into a LAB color model, and the shadows are detected on the basis of the obtained values in the A and B planes. The shadows are then removed by multiplying the R, G and B values corresponding to each shadow region by appropriate constants. The resulting images are displayed in the Figure 7d. Once more, the obtained images are far from an accurate representation of the original concrete images--images are not ghost-free and cracks are removed in places. Various conventional schemes such as homomorphic filtering and retinex, as well as other variational approaches, have been successfully applied to illumination correction for images with shadows, where pre-processing is preferred to extensive shadow removal using highly complex methods. As shown in [49], homomorphic filter algorithm has been developed for removing shadows through the analysis of the relationship between the shadow and the brightness of the image. Another approach, a methodology for shadow removal via the application of the non-local retinex model, is demonstrated in [45; 46]. The main assumption of the retinex models is that the input image is a product of the  
Figure 7: The comparison of different methods for shadow removal from concrete images with cracks and without cracks: original concrete images with shadows are shown in part (**a**), application of the shadow removal methodology proposed in [34] is shown in part (**b**), shadow detection and removal using mean and Gaussian filtering is shown in part (**c**), application of color-model based shadow detection and removal technique [44] is shown in part (**d**), application of the non-local retinex model [45; 46] is shown in part (**e**), application of interactive removal and ground truth for difficult shadow scenes [47] is shown in part (**f**), and ghost-free shadow removal via dual hierarchical aggregation network and shadow matting GAN [48] is shown in part (**g**). illumination and the true reflectance of the object. As shown in [45; 46], retinex formulations based on the application of non-local differential operators are particularly suitable for texture-preserving shadow removal.