基于数字图像处理的冲压工件增强算法研究

传统的图像增强算法在增强图像时,存在丢失细节和增大噪声以及运行时间长等问题,为了满足冲压工件缺陷自动在线检测技术的需要,本文提出了基于Contourlet变换和混沌小生境粒子群优化算法(NCPSO)相融合的图像增强算法。首先,对图像进行Contourlet变换分解,在带通方向子带进行自适应增强来实现对工件图像的增强;然后将NCPSO算法引入,克服了Contourlet变换在图像增强时速度较慢等问题,提高了算法效率。通过实验表明,该方法在增强效果和运算时间上都优于传统的图像增强算法。

Research of Stamping Workpiece Enhancement Algorithm Based on Digital Image Processing

When enhance image by Traditional image enhancement algorithms, there are problems such as details are lost and increase noise and long operation time, in order to meet the needs of the stamping workpiece defect automatic on-line inspection technology, Contourlet transform and NCPSO fusion image enhancement algorithm is proposed. First of all, the image decomposed by Contourlet transform, in the direction of band pass the adaptive enhancement is used to realize the workpiece image enhancement; and then introduce NCPSO algorithm, to overcome the Contourlet transform need long time, which improve the efficiency of the algorithm. Experiments show that this method is superior to the traditional image enhancement algorithms on the enhancement effect and the operation time.