Development of an automatic image enhancement method using singular value decomposition for visual inspection

摘要

It is usually difficult to obtain good images for machine vision inspection in a manufacturing process. In practice, to obtain product characteristics, image enhancement methods are usually selected by trial-and-error or by experience. Therefore, image enhancement methods play a key role in image pre-processing. In this paper, we propose singular value decomposition (SVD) to extract the feature of images to automatically build image enhancement procedures. First, we completed image clustering according to the feature by using SVD. Next, the structural similarity index was used to select the optimal image enhancement method. To verify the procedures, 45 images from literature and local companies were used in the experimental analysis. For contrast value, the statistical analysis showed that the automatic enhancement result has no significant difference with the literature. The average entropy of the image relative to previous research increased to 17.54%. The study results implied that the system could effectively improve the image quality and not over enhancement to produce noise.