基于多特征的彩色唐卡修复图像无参考质量评价方法 
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No-Reference Quality Assessment Method for Inpainting Thangka Image Based on Multiple Features
1 西北民族大学数学与计算机科学学院, 甘肃 兰州 730030
2 西北民族大学中国民族语言文字信息技术教育部重点实验室, 甘肃 兰州 730030
图 & 表
图 1. 唐卡修复图像线描图提取及风格化阈值效果图。(a)唐卡划痕破损图像;(b)唐卡块状破损图像;(c)样本块修复效果图;(d)TV修复效果图;(e)(f)普通DOG算子线描效果图;(g)(h),(i)(j),(k)(l)风格化阈值线描效果图,其中φ值依次为1.2, 1.6和2.0
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
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图 2. 对称融合效果图
Fig. 2. Effect of symmetric fusion image
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图 3. 色彩熵与不同修复图像之间的线性关系图。 (a)块状破损色彩熵变化图;(b)划痕破损色彩熵变化图
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
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图 4. BP-AdaBoost 神经网络的框图
Fig. 4. Structure of the BP-AdaBoost neural network
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图 5. 破损唐卡修复效果。(a)划痕失真图像;(b)块状失真图像;(c) TV模型修复图像;(d)样本块修复图像
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
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图 6. 唐卡数据库中,不同评价算法与其对应的DMOS值的散点图。(a)文献[
15]方法;(b)文献[
26]方法;(c)文献[
16]方法;(d)本文方法
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
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图 7. 不同算法间鲁棒性对比
Fig. 7. Comparison of robustness among different algorithms
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表 1唐卡数据库组成
Table1. Composition of Thangka database
| Damaged model | Inpainting model | Image number | Number of subjective evaluation |
|---|
| Scratch | TV model | 200 | 6 | | Massive | Sample block-based model | 300 | 7 | | Scratch | Sample block-based model | 300 | 8 | | Massive | TV model | 200 | 9 |
|
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表 2唐卡数据库中各种类型修复图像迭代1000次SROCC中值
Table2. SROCC median of different types of repaired images in Thangka database iterate 1000 times
| Method | Scratch-TVmodel | Massive-sampleblock-based model | Scratch-sampleblock-based model | Massive-TVmodel | All |
|---|
| PSNR | 0.8646 | 0.8831 | 0.9210 | 0.7515 | 0.8636 | | SSIM | 0.9389 | 0.9446 | 0.9235 | 0.9046 | 0.9129 | | Method in Ref. [12] | 0.8431 | 0.9391 | 0.9373 | 0.9542 | 0.9545 | | Method in Ref. [13] | 0.9394 | 0.9449 | 0.9272 | 0.9246 | 0.9647 | | ASVS | 0.8935 | 0.9418 | 0.9282 | 0.9424 | 0.8954 | | DN | 0.9040 | 0.9291 | 0.9202 | 0.8983 | 0.9063 | | Method in Ref. [15] | 0.9325 | 0.9411 | 0.9284 | 0.9428 | 0.9326 | | Method in Ref. [26] | 0.9039 | 0.9287 | 0.9316 | 0.9403 | 0.9172 | | Method in Ref. [16] | 0.8999 | 0.9467 | 0.9349 | 0.9435 | 0.9314 | | Proposed method | 0.9327 | 0.9485 | 0.9339 | 0.9741 | 0.9463 |
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表 3唐卡数据库中各种类型修复图像迭代1000次PLCC中值
Table3. PLCC median of different types of repaired images in Thangka database iterate 1000 times
| Method | Scratch-TVmodel | Massive-sampleblock-based model | Scratch-sampleblock-based model | Massive-TVmodel | All |
|---|
| PSNR | 0.8762 | 0.9029 | 0.9173 | 0.7801 | 0.8592 | | SSIM | 0.9405 | 0.9363 | 0.9324 | 0.9004 | 0.9066 | | Method in Ref. [12] | 0.8301 | 0.9268 | 0.9583 | 0.9640 | 0.9511 | | Method in Ref. [13] | 0.9494 | 0.9449 | 0.9372 | 0.9300 | 0.9613 | | ASVS | 0.8546 | 0.9356 | 0.9365 | 0.9688 | 0.8678 | | DN | 0.9041 | 0.9292 | 0.9302 | 0.8983 | 0.9063 | | Method in Ref. [15] | 0.9325 | 0.9211 | 0.9584 | 0.9328 | 0.9326 | | Method in Ref. [26] | 0.9040 | 0.9288 | 0.9516 | 0.9403 | 0.9172 | | Method in Ref. [16] | 0.8645 | 0.9367 | 0.9234 | 0.9868 | 0.9432 | | Proposed method | 0.9446 | 0.9393 | 0.9555 | 0.9795 | 0.9492 |
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表 4不同算法在唐卡数据库上处理一张图片的平均用时
Table4. Average time to process an image by different algorithms on Thangka database
| Method | Average time /s |
|---|
| Method in Ref. [12] | 18.342 | | Method in Ref. [13] | 56.987 | | ASVS | 0.0546 | | DN | 4.8452 | | Method in Ref. [15] | 0.0325 | | Method in Ref. [26] | 63.676 | | Method in Ref. [16] | 1.1645 | | Proposed method | 1.2235 |
|
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叶雨琪, 胡文瑾. 基于多特征的彩色唐卡修复图像无参考质量评价方法[J]. 激光与光电子学进展, 2020, 57(8): 081105. Yuqi Ye, Wenjin Hu. No-Reference Quality Assessment Method for Inpainting Thangka Image Based on Multiple Features[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081105.
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