Abstract:

In recent years， the research on color restoration of mural image has become a hot issue in the fields of mural cultural heritage protection and display. Aiming at the problems that the overall feature information of mural image color restoration is difficult to extract and maintain effectively， and local color restoration is prone to generate false color phenomenon and color spill， a multi-level color restoration method of mural image based on gated positional encoding was proposed. Firstly， an encoder network based on global feature constraints was constructed， and the global feature gradient of the image was extracted as the downsampling value standard by an improved multi-kernel maximum-average-minimum pooling algorithm to establish the mural image feature pyramid， so as to reduce the overall feature loss of the mural image in the feature coding process. Secondly， in order to restore the local color information of mural image accurately， a color transfer module based on gated positional encoding was designed to restrict the learning of similarity kernel between content feature and color feature in spatial domain for accurately mapping of color feature in the mural image to be restored， so as to reduce false color phenomenon and color spill in restored image. Experimental results show that compared with the mural restoration images generated by AdaIN （Adaptive Instance Normalization）， AST （Arbitrary Style Transfer） and other comparison methods， the NIQE （Natural Image Quality Evaluator） and PIQE （Perception based Image Quality Evaluator） in the mural restoration images generated by proposed method achieve the best results. It can be seen that the proposed method has good performance in restoring the color information of mural image and maintaining the global structural and textural characteristics of the mural image to be restored.

Key words: encoder-decoder network, mural image, color restoration, global feature, positional encoding

摘要：

近年来，壁画图像的色彩还原研究已成为壁画文物保护和展示领域的一个热点问题。针对壁画色彩还原面临的整体特征信息难以有效提取和保持，局部色彩还原易出现假色以及色彩溢出等问题，提出基于门控位置编码的壁画图像多级色彩还原方法。首先，构建基于全局特征约束的编码器网络，并通过改进的多核多值池化算法提取图像的全局特征梯度作为下采样取值标准以建立壁画图像特征金字塔，从而减少壁画图像在特征编码过程中的整体特征损失；其次，为准确还原壁画图像的局部色彩信息，设计基于门控位置编码的色彩迁移模块，该模块通过约束空间域中内容特征与色彩特征之间相似性核的学习，构建色彩特征在待还原壁画图像中的准确映射，从而减少还原图像中的假色现象与色彩溢出。实验结果表明，该方法所生成的壁画还原图像相较于AdaIN（Adaptive Instance Normalization）、AST（Arbitrary Style Transfer）等对比方法所生成的壁画还原图像，NIQE（Natural Image Quality Evaluator）和PIQE（Perception based Image Quality Evaluator）都取得了最优的结果。可见，所提方法能有效还原壁画色彩信息并保持待还原壁画图像的整体结构纹理特征。

关键词: 编码器-解码器网络, 壁画图像, 色彩还原, 全局特征, 位置编码