Image deblurring by multi-scale modified U-Net using dilated convolution.

In modern urban traffic systems, intersection monitoring systems are used to monitor traffic flows and track vehicles by recognizing license plates. However, intersection monitors often produce motion-blurred images because of the rapid movement of cars. If a deep learning network is used for image deblurring, the blurring of the image can be eliminated first, and then the complete vehicle information can be obtained to improve the recognition rate. To restore a dynamic blurred image to a sharp image, this paper proposes a multi-scale modified U-Net image deblurring network using dilated convolution and employs a variable scaling iterative strategy to make the scheme more adaptable to actual blurred images. Multi-scale architecture uses scale changes to learn the characteristics of different scales of images, and the use of dilated convolution can improve the advantages of the receptive field and obtain more information from features without increasing the computational cost. Experimental results are obtained using a synthetic motion-blurred image dataset and a real blurred image dataset for comparison with existing deblurring methods. The experimental results demonstrate that the image deblurring method proposed in this paper has a favorable effect on actual motion-blurred images.

A Study of Defect Detection Techniques for Metallographic Images.

Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.