Optimization Strategy for X-Ray Generation and Countermeasure Fusion of Bronze Mirror

Meng Wu; Jiao Wang; Jiankai Xiang

doi:10.3788/LOP212843

Laser & Optoelectronics Progress, Volume. 60, Issue 2, 0234001(2023)

Optimization Strategy for X-Ray Generation and Countermeasure Fusion of Bronze Mirror

Meng Wu^1、*, Jiao Wang¹, and Jiankai Xiang²

¹College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China

²Shaanxi Institute for the Preservation of Cultural Heritage, Xi'an 710075, Shaanxi, China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. X-ray imaging effect diagrams of copper mirror at different shooting energy. (a) High-energy shooting; (b) low-energy shooting

Download full size

Fig. 2. Copper mirror generative confrontation fusion network framework

Download full size

Fig. 3. Generator network structure based on multi-scale feature fusion

Download full size

Fig. 4. Discriminator network structure

Download full size

Fig. 5. Network training process

Download full size

Fig. 6. Copper mirror X-ray image control group. (a) High energy shooting effect group; (b) low energy shooting effect group

Download full size

Fig. 7. Comparison of copper mirror fusion by different algorithms. (a) LP; (b) GFF; (c) LE-LP; (d) BFWLS; (e) O-BFWLS

Download full size

Fig. 8. Comparison of copper mirror fusion in ablation experiment. (a) Fusion-GAN-CLF; (b) Fusion-GAN-MSFF; (c) proposed algorithm

Download full size

Fig. 9. Comparison of fusion effect details. (a) High-energy X-source image; (b) low-energy X-source image; (c) Fusion-GAN-CLF; (d) Fusion-GAN-MSF; (e) proposed algorithm

Download full size

Table 1. Objective evaluation results of different algorithms on different images

View table

Table 1. Objective evaluation results of different algorithms on different images

Image

Metric

GFF

LE-LP

BFWLS

O-BFWLS

Group 1

CEN

NFMI

4.9667

10.6807

2.0742

2.566/2.971

7.915/7

1.9646

4.857

10.8896

2.2773

0.590/6.059

7.584/7.092

2.1598

4.8333

9.9375

1.9118

1.227/5.112

7.590/7

2.0651

4.4198

9.1235

1.5558

0.244/6.315

6.597/6.447

2.5458

4.4291

9.1596

1.6132

0.204/6.341

6.576/6.455

2.5696

Group 2

CEN

NFMI

4.2420

6.8456

2.0520

0.193/0.303

7.175/5.809

1.9256

4.1233

7.4411

1.9256

0.525/0.457

6.882/5.936

2.0003

4.205

7.2289

1.9665

0.467/0.469

6.879/5.918

2.1584

4.4404

6.2832

1.5032

0.538/0.562

6.100/5.763

2.856

4.2231

6.2832

1.5032

0.531/0.541

5.773/5.759

3.138

Group 3

CEN

NFMI

5.1207

6.8398

1.8539

0.047/0.550

7.3246/7

3.0145

5.8605

1.8432

0.455/0.590

8.3765/7

2.1592

4.9781

5.8595

1.7057

1.230/0.448

7.8212/7

2.7046

5.1144

6.5366

1.4828

0.052/0.564

7.2544/7

3.2308

5.1144

6.5366

1.4828

1.695/0.571

5.0175/7

4.9991

Group 4

CEN

NFMI

6.0487

6.4464

1.5824

1.411/1.095

11.36/9.669

1.1782

5.6106

8.6505

1.7474

3.461/0.806

10.86/9.155

0.9797

5.5615

8.2303

1.6409

1.364/1.266

10.70/8.996

0.8909

5.4072

8.3238

1.3300

0.097/1.631

10.70/8.995

1.002

5.4152

8.1558

1.4989

0.065/1.483

10.73/9.021

0.9821

Group 5

CEN

NFMI

3.9007

5.5334

1.3779

0.437/0.338

6.177/5.645

1.8198

3.6696

6.0313

1.2521

0.202/0.193

5.901/5.443

1.8778

3.7686

5.5984

1.2257

0.700/0.340

5.658/5.343

2.1215

3.735

6.0035

1.1408

0.149/0.544

5.126/5.198

2.4949

3.7331

6.0789

1.2949

0.058/0.541

4.623/5.197

2.9579

Table 2. Comparison of experimental data of different algorithms

View table

Table 2. Comparison of experimental data of different algorithms

Image

Metric

Fusion-GAN-CLF

Fusion-GAN-MSFF

Proposed algorithm

Group 1

CEN

NFMI

4.5475

10.399

2.1427

9.493/1.006

7.832/6.872

2.9488

4.5868

10.0421

1.9867

9.455/1.278

7.770/6.839

3.0166

4.5875

10.7134

2.222

9.657/1.021

7.858/6.905

2.9875

Group 2

CEN

NFMI

4.2864

8.6157

2.1602

12.37/15.60

6.865/5.250

2.3429

4.2724

8.4269

2.0118

12.43/15.71

6.839/5.252

2.3629

4.3101

8.7189

2.1705

12.33/15.55

6.914/5.259

2.3559

Group 3

CEN

NFMI

5.942

10.2126

3.4667

9.674/13.42

8.055/6.836

2.537

5.933

9.4506

3.0807

9.447/13.35

7.898/6.800

2.5786

5.9653

10.3992

3.5488

9.438/13.23

8.055/6.846

2.6648

Group 4

CEN

NFMI

6.3531

7.5210

1.7487

5.848/12.71

10.60/8.908

0.9464

6.3967

7.7080

1.6680

6.021/12.72

10.65/8.958

0.9635

6.4849

7.7082

1.7529

6.246/12.75

10.75/9.052

0.9989

Group 5

CEN

NFMI

3.7859

8.6920

1.6906

12.75/15.63

5.768/4.910

2.2131

3.7733

8.5561

1.6821

12.71/15.65

5.748/4.893

2.2188

3.7878

9.3739

1.6934

12.74/15.63

6.262/5.685

2.2303

Table 3. Subjective score of experts in cultural relic restoration

View table

Table 3. Subjective score of experts in cultural relic restoration

Level	Hinder the scale	Score
Excellent	Observe all diseases and identify types	5
Good	Observe all diseases and identify some types	4
Moderate	Observe part of the disease and identify the type	3
Pass	Observe part of the disease but cannot distinguish the type	2
Poor	Unable to observe the disease	1

Table 4. Subjective evaluation results
View table
Table 4. Subjective evaluation results
Group Fusion-GAN-CLF Fusion-GAN-MSFF Proposed algorithm
Group 1 4 3.8 4.7
Group 2 4.2 4.4 4.8
Group 3 3.8 3.9 4.4
Group 4 3.6 3.4 4.2
Group 5 4.2 3.9 4.6

Tools

Get Citation

Copy Citation Text

Meng Wu, Jiao Wang, Jiankai Xiang. Optimization Strategy for X-Ray Generation and Countermeasure Fusion of Bronze Mirror[J]. Laser & Optoelectronics Progress, 2023, 60(2): 0234001

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites