# Context-Encoder-Based Image Inpainting for Ancient Chinese Silk
## 2 Related Work  
The inception of deep learning in the image inpainting domain was marked by the introduction of Context-Encoders [11], leveraging the encoder-decoder neural network framework originally posited by [PERSON] et al. in 1986 [12]. This seminal architecture, comprising an input layer, multiple hidden layers, and an output layer, was initially conceptualized for extracting salient features and reducing data dimensionality. Its subsequent adaptation for image inpainting tasks has underscored its versatility and efficacy in reconstructing coherent structures within damaged or incomplete image data. In the context of image defect restoration, the Context-Encoder within the network adeptly leverages both local and global image information, seamlessly aligning the generated data with the original image. Algorithms grounded in the Context-Encoder structure have gained prominence. A structural representation is shown in Figure 1. The fully connected channel between the encoder and decoder effectively reduces the number of network parameters. The Context-Encoder seamlessly integrates intricate image details around the defective area and semantic information spanning the entire image. During the training phase, Context-Encoders incorporate reconstruction loss for consistency between the overall structure and context of the missing regions. Additionally, Encoders introduce adversarial loss to enhance the flexibility of the generated data. The overarching loss function of this approach combines the aforementioned reconstruction and adversarial losses through a weighted combination. Subsequently, [PERSON] et al. [13]. extended the framework of Context-Encoders by incorporating an edge-centric context decoder. Their methodology commences with extracting edge information from the image with Fully Convolutional Networks (FCNs) [14] to restore  
Figure 1: The architecture of the Context-Encoder framework. missing details. The recovered edge information seamlessly integrates with the incomplete image through the Context-Encoder, contributing to further refinement. To address challenges associated with pronounced negative responses during training, [PERSON] et al. [15] strategically replaced the ReLU and Leaky ReLU layers in Context-Encoders with ELU layers, strengthening overall model stability. Furthermore, [PERSON] et al. [16]. introduced a novel linear combination of pixel reconstruction loss and feature reconstruction loss, designated as structural loss, building on the foundational Context-Encoder framework to significantly improve the precision of the model. To enhance the homogeneity of the structure and the post-repair image, recently [PERSON] et al. [17] proposed rethinking image inpainting via a Mutual Encoder-Decoder with Feature Equalizations (MEDFE), anchored in a mutual encoding-decoding network structure. Shallow encoder-learned features represent texture, while deep features comprehensively encapsulate structural details. A feature equalization method was introduced to ensure consistency between the structural and texture features, which involved reweighting channels post-feature-concatenation and introducing a bilateral propagation activation function. These modifications effectively eradicate blurriness and artifacts arising from inconsistent structural and texture features. Continuing the discourse, [PERSON] et al. [18] introduced an image inpainting model with Learning to Incorporate Structure Knowledge (LISK), incorporating a structure-embedding scheme for enhanced fusion of structural information. The model implements an attention mechanism for nuanced adjustments to the generated structure and content, accompanied by a novel pyramid structure loss designed to oversee acquisition of structural knowledge in image restoration. Meanwhile, [PERSON] et al. [19] proposed a Mask-Aware Dynamic Filtering (MADF) module for efficient feature learning in the encoder. Additionally, they introduced point-wise normalization (PN) to refine batch normalization, preventing disappearance of the semantic layout.