基于稀疏表示的Data Matrix码图像修复算法  

A Code Image Restoration Algorithm of Data Matrix Based on Sparse Representation

作　　者：陈庆然 许义宝 李新华[1] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,安徽合肥230601

出　　处：《计算机技术与发展》2018年第1期60-63,68,共5页Computer Technology and Development

基　　金：国家自然科学基金(61272317)

摘　　要：稀疏表示理论凭借其建模简单、鲁棒性高与抗干扰能力强等优势成为研究热点,将稀疏理论应用于图像修复已成为图像处理领域新的研究方向。针对工业现场中常出现的被遮挡而不能识别的二维码图像,提出一种基于稀疏表示模型的块聚类图像修复算法。依据待修复图像内的有效信息,以固定重叠像素的方式将图像分块,分别对图像块使用欧几里得距离进行训练匹配,将得到的具有相似结构的图像块聚类为结构组作为图像稀疏表示的基本单位,利用每个结构组的估计来快速学习字典。通过使用分离迭代与优化梯度算法对组稀疏表示模型的L1范数最小化问题进行求解,提高了修复算法的鲁棒性。实验结果表明,该算法能够很好地修复被遮挡、划痕或像素丢失等受损的Data Matrix码图像,较大地提高了条码的识别率。Sparse representation theory has become a hot topic in the field of image processing because of its simple modeling, high robust- ness and strong anti-jamming,which is applied in image restoration as a new research direction in image processing. In view of the blocked two-dimensional code image with no identification which often appear in the industrial scene, we propose an image restoration algorithm of block clustering based on sparse representation model. According to the effective information in the image to be repaired, the image is seg- mented by fixed overlapping pixels, and Euclidean distance is used to train and match image blocks. Taking the obtained image blocks with similar structure clustered into structural groups as the basic unit of image sparse representation, fast dictionary learning is carried out by using the estimation of each structure group. By using the separation iteration and the optimized gradient algorithm, the L1 norm-minimization of the group sparse representation model is solved, improving the robustness of restoration algorithm. The experiment shows that the proposed algorithm can repair the damaged Data Matrix code images such as occlusion, scratches or pixel loss, which greatly improves the recognition rate of bar code.

关 键 词：DATA Matrix码 图像修复 块聚类 稀疏表示 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]