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The process of removing occluding hair has a relevant role in the early andaccurate diagnosis of skin cancer. It consists of detecting hairs and restorethe texture below them, which is sporadically occluded. In this work, wepresent a model based on convolutional neural networks for hair removal indermoscopic images. During the network's training, we use a combined lossfunction to improve the restoration ability of the proposed model. In order totrain the CNN and to quantitatively validate their performance, we simulate thepresence of skin hair in hairless images extracted from publicly known datasetssuch as the PH2, dermquest, dermis, EDRA2002, and the ISIC Data Archive. As faras we know, there is no other hair removal method based on deep learning. Thus,we compare our results with six state-of-the-art algorithms based ontraditional computer vision techniques by means of similarity measures thatcompare the reference hairless image and the one with hair simulated. Finally,a statistical test is used to compare the methods. Both qualitative andquantitative results demonstrate the effectiveness of our network.

An Encoder-Decoder CNN for Hair Removal in Dermoscopic Images