The appearance of specular highlights in images is one main factor affecting accurate material or object recognition tasks. Such an appearance has a misleading effect on the true gradient information found in transmissive material images. Certain methods use specular highlights as an intrinsic feature of transparency to detect transparent objects. However, this process reduces the robustness of methods in applications with opaque and shiny materials and in the classification of tasks among related features, such as transparency and translucency. Thus, correcting this artefact can enhance texture- or gradient-based image and video analyses. However, the correction of small or large regions with specular highlights from transmissive materials, such as glass, plastic and water, is complex and ambiguous. These materials are sensitive to specular highlights and exhibit high degrees of reflection. In this study, we propose a deep learning framework to address the problem. A partial convolution-based inpainting method is integrated with automatic semantic mask generation by using a simple adaptive binarization to detect highlight spots during training and inference. The proposed framework improves the learning process by capturing the semantic nature of specular highlights. Moreover, the framework eliminates the use of image-mask pairs during inference and avoids predefined irregular random mask training. We qualitatively and quantitatively evaluate the proposed framework by using new and existing publicly available datasets that contain specular images. Experimental results show that our framework registers competitive performance and considerably reduces computational time.

中文翻译：

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一种从透射材料中去除镜面高光的深度学习方法

图像中镜面高光的出现是影响准确材料或物体识别任务的主要因素之一。这种外观对透射材料图像中发现的真实梯度信息具有误导作用。某些方法使用镜面高光作为透明度的固有特征来检测透明物体。然而，这个过程降低了方法在不透明和闪亮材料应用中的鲁棒性，以及在相关特征（例如透明度和半透明性）之间的任务分类中的鲁棒性。因此，纠正此人工制品可以增强基于纹理或梯度的图像和视频分析。然而，对具有透射材料（例如玻璃、塑料和水）的镜面高光的小区域或大区域进行校正是复杂且不明确的。这些材料对镜面高光很敏感，并表现出高度反射。在这项研究中，我们提出了一个深度学习框架来解决这个问题。通过使用简单的自适应二值化来检测训练和推理过程中的亮点，将基于部分卷积的修复方法与自动语义掩码生成相结合。所提出的框架通过捕捉镜面高光的语义性质改进了学习过程。此外，该框架在推理过程中消除了图像掩码对的使用，并避免了预定义的不规则随机掩码训练。我们通过使用包含镜面反射图像的新的和现有的公开数据集来定性和定量地评估所提出的框架。