The art of Dunhuang Grottoes is mainly composed of three forms: building, sculpture, and murals. With the passage of time, the art of Dunhuang Grottoes has suffered from tremendous damage, and making it difficult to repair. With Virtual Reality, we can even observe the digital preservation of Dunhuang Caves in virtual environment as if we are in Dunhuang by modeling the caves and preserve mural as textures. However, we still cannot have a glimpse of how the grottoes look like without damage. With the ever-changing nature of deep learning, it is widely used in many fields. In this work, we propose a systematic restoration process for high-resolution types of deteriorated mural textures. We use the Generative Adversarial Network technology in deep learning. By making the machine learn the transformation between deteriorated mural textures and restored mural textures, we can simultaneously produce high-resolution but color-inconsistent, as well as low-resolution but color-consistent results. After post-processing through digital image processing technology, we can get both high-resolution and color-consistent texture.
目次
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Mural Restoration System 4 2.2 Digital Image Processing 4 2.3 Deep Learning 5 2.3.1 Auto Encoder 7 2.3.2 Generative Adversarial Network 8 Chapter 3 Dunhuang Mural Restoration 11 3.1 System Overview 11 3.2 Data Pre-processing 11 3.3 Training Process 15 3.3.1 Pre-train on Paired Training Data 16 3.3.2 Train on Whole Training Data 17 3.4 Inference Process 19 Chapter 4 Experiments 21 4.1 Observation 21 4.1.1 Padding 21 4.1.2 Resolution 22 4.1.3 Color Composition Preservation 24 4.1.4 Sharpness Enhancement 27 4.2 Classification First 30 Chapter 5 Conclusion and Future Work 32 5.1 Conclusion 32 5.2 Future Work 33 5.2.1 Super-resolution Generative Adversarial Network 33 5.2.2 Limited Pigment Constrain 35 Appendix 36 Comparison with L1 and L2 loss 36 61th Cave Dunhuang Mural Restoration 40 REFERENCE 44
