# Hair Removal Combining Saliency, Shape and Color
## 1 Introduction
The binary image is obtained through a statistical thresholding process (step 2). To extract the hairs, the elongate feature property of connected regions is employed (step 3). To restore the information occluded by the hair, they apply the image inpainting method based on partial differential equation (PDE), which realizes the diffusion of information through the difference between pixels (step 4). In the method proposed in [20] by [PERSON] et al., there are three computational steps. In the CIELab uniform color space, the hairs are detected by a derivative of Gaussian (DoG) (step 1). Morphological techniques to link broken hair segments, eliminate small and circular objects, and fill the gaps between lines are applied (step 2). The adopted inpainting method is based on coherence transport (step 3). The method in [21] by [PERSON] et al. comprises three steps. To the grayscale version of the image, a multiscale curvilinear matched filter is applied (step 1). To detect the hair regions, hysteresis thresholding is employed (step 2). Then, region growing and the linear discriminant analysis (LDA) technique, based on the pixel color information in the CIELab color space, are applied to recover the missing information left by the removed hair (step 3). Figure 1: (a) Examples of images (IMD003 and ISIC_0002871) with a massive presence of hair; (b) the resulting image after the application of saliency shape color for hair removal (HR-SSC). The method [22] by [PERSON] et al. includes four steps. The image is converted to a grayscale image via a principal component analysis (PCA), and the noise is filtered with a Wiener filter (step 1). Hair is detected by using an adaptive canny edge detector (step 2). A refining process with morphological operators to eliminate unwanted objects and obtain a smooth hair mask is then applied (step 3). The inpainting process is carried out by a multi-resolution transport inpainting method based on wavelets (step 4). As with [20; 21], in [23] by [PERSON] et al., hair removal is made up of three steps. The contrast of the luminance of the image is improved with the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm (step 1). The hair is detected using soft color morphology operators in the CIELab color space (step 2). The inpainting phase is based on the arithmetic mean of the modified opening and closing morphological transformations to recover the missing pixels (step 3). The common element of these HR methods and most of the other existing methods, e.g., [24; 25], is the employment of morphological operations and, to a minor extent, of information derived from color. On the other hand, although deep learning has been used successfully to solve many difficult computer vision problems, inexplicably, to the best of our knowledge, only two very recent HR methods relying on neural network architecture exist [26; 27]. Despite the sufficiently wide variety of the existing papers, the problem of hair removal results to be not solved satisfactorily yet. The main critical points are the failure to identify hair accurately and the undesirable effects such as unremoved thin hair and color alteration. We address the HR problem using information regarding the saliency, shape, and color of the image objects. These are three elements that have proved to be extremely useful because each of them allows capturing a fundamental aspect of the problem at hand. Indeed, besides the shape aspects, detectable by mathematical morphology properties, it is also appropriate to perform the hair detection based on information related to the significant image elements and detectable by their saliency and color properties. In the following, we refer to the proposed method as saliency shape color for hair removal, shortly indicated as HR-SSC or simply SSC. As described in Section 2, HR-SSC consists of five steps. The core of the method is step 4, named hair object detection, in which the hair regions are determined.