Automated skin lesion analysis is one of the trending fields that has gained attention among the dermatologists and health care practitioners. Skin lesion restoration is an essential pre-processing step for lesion enhancements for accurate automated analysis and diagnosis by both dermatologists and computer-aided diagnosis tools. Hair occlusion is one of the most popular artifacts in dermatoscopic images. It can negatively impact the skin lesions diagnosis by both dermatologists and automated computer diagnostic tools. Digital hair removal is a non-invasive method for image enhancement for decrease the hair-occlusion artifact in previously captured images. Several hair removal methods were proposed for skin delineation and removal without standardized benchmarking techniques. Manual annotation is one of the main challenges that hinder the validation of these proposed methods on a large number of images or against benchmarking datasets for comparison purposes. In the presented work, we propose a photo-realistic hair simulator based on context-aware image synthesis using image-to-image translation techniques via conditional adversarial generative networks for generation of different hair occlusions in skin images, along with ground-truth mask for hair location. Hair-occluded image is synthesized using the latent structure of any input hair-free image by deep encoding the input image into a latent vector of features. The locations of required hair are highlighted using white pixels on the input image. Then, these deep encoded features are used to reconstruct the synthetic highly realistic hair-occluded image. Besides, we explored using three loss functions including L₁-norm, L₂-norm and structural similarity index (SSIM) to maximize the image synthesis visual quality. For the evaluation of the generated samples, the t-SNE feature mapping and Bland–Altman test are used as visualization tools for the experimental results. The results show the superior performance of our proposed method compared to previous methods for hair synthesis with plausible colours and preserving the integrity of the lesion texture. The proposed method can be used to generate benchmarking datasets for comparing the performance of digital hair removal methods. The code is available online at: https://github.com/attiamohammed/realhair.

自动皮肤病变分析是皮肤科医生和医疗保健从业者关注的趋势领域之一。皮肤病灶修复是皮肤科医生和计算机辅助诊断工具进行准确自动分析和诊断的病灶增强必不可少的预处理步骤。毛发遮挡是皮肤镜图像中最流行的伪影之一。它会对皮肤科医生和自动计算机诊断工具对皮肤病变的诊断产生负面影响。数字脱毛是一种用于图像增强的非侵入性方法，用于减少先前捕获的图像中的毛发遮挡伪影。在没有标准化基准技术的情况下，提出了几种脱毛方法用于皮肤描绘和去除。手动注释是阻碍在大量图像上验证这些建议方法或针对基准数据集进行比较的主要挑战之一。在目前的工作中，我们提出了一种基于上下文感知图像合成的逼真头发模拟器，使用图像到图像转换技术，通过条件对抗生成网络在皮肤图像中生成不同的头发遮挡，以及用于头发位置。通过将输入图像深度编码为特征的潜在向量，使用任何输入无毛发图像的潜在结构合成毛发遮挡图像。所需头发的位置使用输入图像上的白色像素突出显示。然后，这些深度编码的特征用于重建合成的高度逼真的头发遮挡图像。此外，L _{1 -}范数、L _{2 -}范数和结构相似性指数（SSIM）最大化图像合成视觉质量。对于生成的样本的评估，t-SNE 特征映射和 Bland-Altman 测试被用作实验结果的可视化工具。结果表明，与以前的头发合成方法相比，我们提出的方法具有更优越的性能，具有合理的颜色并保持病变纹理的完整性。所提出的方法可用于生成用于比较数字脱毛方法性能的基准数据集。该代码可在线获取：https://github.com/attiamohammed/realhair。