No-Reference Quality Assessment Method for Inpainting Thangka Image Based on Multiple Features

Yuqi Ye; Wenjin Hu

doi:10.3788/LOP57.081105

Laser & Optoelectronics Progress, Volume. 57, Issue 8, 081105(2020)

No-Reference Quality Assessment Method for Inpainting Thangka Image Based on Multiple Features

Yuqi Ye¹ and Wenjin Hu^1,2、*

Author Affiliations

¹School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu 730030, China

²Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu 730030, China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(11)

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

Download full size

Fig. 2. Effect of symmetric fusion image

Download full size

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

Download full size

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

Download full size

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

Download full size

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

Download full size

Fig. 7. Comparison of robustness among different algorithms

Download full size

Table 1. Composition of Thangka database
View table
Table 1. 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

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

View table

Table 2. 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

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

View table

Table 3. 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

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

Tools

Get Citation

Copy Citation Text

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

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Imaging Systems

Received: Jul. 19, 2019

Accepted: Sep. 24, 2019

Published Online: Apr. 3, 2020

The Author Email: Wenjin Hu (wenjin_zhm@126.com)

DOI:10.3788/LOP57.081105

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Composition of Thangka database

Table 1. Composition of Thangka database

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

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 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

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