关键词: 织物图像, 重构, 一致受限字典, 变分贝叶斯期望最大化, 收缩因子

Abstract: With the culmination of digital transformation, the textile industry, as an important component of the manufacturing sector, is gradually moving towards the field of intelligent manufacturing. By introducing advanced digital technologies and automation systems, the textile industry can achieve high efficiency and precision in the production process. The application of automation equipment and robots can reduce human errors and labor costs while improving production efficiency. With continuous technological advancements and changing market demands, the textile industry is facing numerous challenges and opportunities. The advent of big data has led to a significant increase in data volume, which poses a significant burden on intelligent manufacturing. Additionally, the increased volume of image data in particular can lead to compression distortion during the transmission process. To address this, compressing images using sparse representation technology can avoid wastage of resources during transmission. Sparse reconstruction, as the inverse problem of sparse representation, is crucial for accurately restoring the sparse-represented image data without losing the original information.
To enhance the core competitiveness of the textile industry, this paper proposed a VBEM (variational Bayesian expectation maximization)-based consistent constrained dictionary (CCD-VBEM) model for fabric image reconstruction. It addressed the problem of decreased reconstruction performance caused by strong inter-column consistency in traditional sparse Bayesian algorithms. Considering the real-world application scenarios of fabric images, a multi-layer prior sparse Bayesian learning (SBL) model was adopted for modeling, and the VBEM method was used to approximate the posterior distribution. This resulted in the construction of the SBL-VBEM model. However, the reconstruction results of the SBL-VBEM model are still affected by the coherence of the dictionary matrix. To improve the reconstruction results, this paper reduced the inter-column consistency of the dictionary matrix.
To achieve this goal, the paper first obtained a shrinkage factor using the topological structure of the sigmoid function. With the shrinkage factor, the neighborhood interval of the largest off-diagonal entry in the dictionary matrix can be reduced at each iteration of obtaining the consistent constrained dictionary. This effectively reduces the inter-column consistency, thereby improving the quality of the reconstruction results. Finally, the obtained consistent constrained dictionary was used as input for the SBL-VBEM model to reconstruct fabric images more effectively. The effectiveness of this approach was validated on the Alibaba Cloud Tianchi dataset. Experimental results demonstrate that the CCD-VBEM method achieves optimal performance in reconstructing fabric images at different sampling rates (0.20–0.40), showcasing the potential of the algorithm in the field of fabric image reconstruction.

Key words: fabric image, reconstruction, consistent constrained dictionary, variational Bayesian expectation maximization, shrinkage factor