A Feature Disentanglement Method for Image Stitching of Printed Circuit Boards

Image stitching plays a pivotal role in the quality inspection of printed circuit boards (PCBs). However, this process encounters significant challenges owing to the limited overlapping regions between the stitched images. Existing methods aim to alleviate the influence of features from nonoverlapping regions on network performance by regularizing the loss function for these regions. Nevertheless, this approach incompletely eradicates interference from nonoverlapping regions. Moreover, PCB images often comprise numerous homogeneous regions, where indistinguishable semantic information can result in erroneous alignments between feature points. In this article, we propose a novel framework from the perspective of learning disentangled representations. Through an image reconstruction task, we disentangle the matching features of overlapping regions from the interfering features of nonoverlapping regions, thereby attaining purified matching features and eliminating noncontributory features. Specifically, we partition the encoded features of the image into shared matching features from overlapping regions and interfering features from nonoverlapping regions. By exchanging the shared features between image pairs to reconstruct the feature representations, we utilize these reassembled features to reconstruct the input image pairs. Furthermore, to address the ambiguity of homogeneous features in overlapping regions, we devise a frequency contextual correlation module (FCCM). The frequency convolution kernels in this module compute correlations between pixel blocks rather than individual pixels. Consequently, FCCM adeptly captures contextual semantic information between feature maps, resolving ambiguities induced by feature points in homogeneous regions. Extensive experimentation demonstrates that our approach effectively segregates matching alignment features from the overall image features while mitigating the interference of homogeneous region features. This contribution enhances alignment accuracy and achieves a 100% stitching success rate for PCBs, satisfying industrial requirements.