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Thangka murals are precious cultural heritage for Tibetan history, literature, and art. Digital line drawing of Thangka murals plays a vital role not only as an abstracted expression of Thangka for art appreciation but also as a fundamental digital resource for Thangka protection. Digital Thangka line drawing can be categorized as image edge detection, which as a fundamental problem for computer vision, aims to extract visually salient edges from images. Varieties of high-level computer vision tasks depend on edge detection. Although existing non-learning and learning-based edge detection methods have progressed, they failed to generate semantically plausible thin edges, especially thin in-object edges. We propose a novel deep supervised edge detection solution Richer In-object Thin Edge Network (RITE-Net) to generate line drawings of Thangka mural images. Compared to existing studies, firstly a new Cross Dense Residual architecture (CDR) is proposed to propagate abundant edge features effectively from shallow layers to deep layers of CNN using a long-range feature memory; Secondly, a new Hard Pixel Balancing (HPB) based loss function strategy is designed to focus on hard pixel distinguishment. Experiments and tests on different datasets show that the proposed RITE-Net is able to produce more visually plausible and richer thin edge maps comparing to the existing methods. Both objective and subjective evaluations validated the competitive performance of our method.

Thangka Mural Line Drawing Based on Cross Dense Residual Architecture and Hard Pixel Balancing