计算机科学与探索2025，Vol.19Issue(6)：1553-1563,11.DOI:10.3778/j.issn.1673-9418.2406095
BMTA:多元场景下的大面积破损图像修复
BMTA:Inpainting of Large Area Damaged Images in Multiple Scenarios
摘要
Abstract
Aiming at the problems of incoherent semantic connection between image pixels and ineffective restoration effect of local texture details in large-scale damaged images,this paper proposes a single-stage image restoration network model named BMTA(block of multi-transformer attention).It can be used to repair a large area of damaged images in multiple scenes,so that the repaired images can have a good performance in the subjective perception of human eyes and objective evaluation indicators.The generator module performs feature compression,reconstruction and enhancement of important feature information of the input image by interspersing dual unidirectional attention modules in the convolution layer.The compressed feature information is divided into channels for local feature extraction and global feature extraction.The global information connection is established by using the segmented fringe window,and the local detail information is ex-tracted with depth by using residual dense blocks.The extracted features are fused.In the decoder part,in order to prevent the local information loss caused by the decoding process and the inaccurate understanding of the context information dur-ing the restoration process,the gated linear self-attention module is used to ensure the multi-level retention of information in the network,so as to achieve the restoration effect closer to the original image.Finally,a discriminator is used to evalu-ate the repair results and promote better expressiveness of the repaired images in terms of structure and texture.The pro-posed method in this paper performs better than the current advanced image restoration algorithms on CelebA,Street-View,and Places2 datasets.关键词
图像修复/注意力机制/Transformer/特征提取Key words
image inpainting/attention mechanism/Transformer/feature extraction分类
信息技术与安全科学引用本文复制引用
曹岩,辛子昊,邬开俊,单宏全,郭炳森..BMTA:多元场景下的大面积破损图像修复[J].计算机科学与探索,2025,19(6):1553-1563,11.基金项目
甘肃省自然科学基金(23JRRA913) （23JRRA913）
内蒙古自治区重点研发与成果转化计划项目(2023YFSH0043). This work was supported by the Natural Science Foundation of Gansu Province(23JRRA913),and the Key Research and Development and Achievement Transformation Program of Inner Mongolia Autonomous Region(2023YFSH0043). （2023YFSH0043）
