焊缝广泛存在于工业现场,基于X射线检测的焊缝质量检测对维护生产生活的正常运行至关重要。然而,高质量的X射线检测图像比特数高,因此,焊缝图像处理系统存在读取缓慢、功耗高和实时性差等问题。提出了一种基于沃尔什-哈达玛变换的焊缝X射线图像压缩方法。该方法基于沃尔什-哈达玛变换的能量集中性质实现图像优化,保留焊缝图像的重点特征边界;利用量子粒子群算法实现对焊缝图像沃尔什-哈达玛变换的压缩参数自适应寻优。实际生产现场图像的验证结果表明压缩图像大小为原图像的十分之一时,所提方法能最大程度地保留焊缝特征与图像质量,能提高基于X射线图像的焊缝检测效率。

Welds exist widely in industrial sites, and the quality inspection of welds based on X-ray flaw detection is very important to maintain the normal operation of production and life. However, high-quality X-ray flaw detection images have high bit counts. Therefore, the weld image processing system has problems such as slow reading, high power consumption and poor real-time performance. Therefore, this paper proposed a welding seam X-ray image compression method based on Walsh-Hadamard transform. Image optimization was realized based on the energy concentration property of Walsh-Hadamard transform, and key feature boundaries of weld images were preserved. The quantum particle swarm optimization algorithm was used to realize the adaptive optimization of the compression parameters of the Walsh-Hadamard transformation of the weld image. The actual production site image verification results showed that while the size of the compressed image was one-tenth of the original image, the method in this paper can preserve the weld features and image quality to the greatest extent, making the subsequent weld detection based on X-ray images more efficient.