Fig. 1. Line drawing of inpainting Thangka image and threshold effect images. (a) Scratch damaged of Thangka image; (b) massive damaged of Thangka image; (c) inpainting image based on sample block-based model; (d) inpainting image based on TV model ; (e)(f)line drawing of DOG operator; (g)(h), (i)(j), (k)(l) threshold effect image, φ=1.2, 1.6, and 2.0,respectively

Fig. 2. Effect of symmetric fusion image

Fig. 3. Linear diagram between color entropy and different inpainting image. (a) Color entropy diagram of massive damage; (b) color entropy diagram of scratch damage

Fig. 4. Structure of the BP-AdaBoost neural network

Fig. 5. Image restoration of damaged Thangka. (a) Scratch damaged of Thangka image; (b) massive damaged of Thangkaimage; (c) repaired image based on TV model; (d) repaired image based on sample block-based model

Fig. 6. Scatter plots of different evaluation algorithms and corresponding DMOS values on Thangka database. (a) Method in Ref. [15]; (b) method in Ref. [26]; (c) method in Ref. [16]; (d) proposed method

Fig. 7. Comparison of robustness among different algorithms

Table 1. Composition of Thangka database

Table 2. SROCC median of different types of repaired images in Thangka database iterate 1000 times

Table 3. PLCC median of different types of repaired images in Thangka database iterate 1000 times

Table 4. Average time to process an image by different algorithms on Thangka database