Image super resolution by double dictionary learning and its application to tool wear monitoring in micro milling

Abstract

It is an effective means to improve the machining quality of product by monitoring the tool wear conditions in micro milling. However, due to the small size and high speed of the micro milling cutter, it is difficult to capture the clear image of tool wear by CCD camera. Therefore, a double dictionary sparse coding super-resolution (SR) method is proposed to improve the resolution of micro milling tool wear images in this work. Based on the traditional single dictionary, this work proposes the residual dictionary to reconstruct the residual image of the tool, achieving the double dictionary coefficient encoding reconstruction. In order to extract local features of low resolution (LR) tool wear images, the Gobor filter banks are designed, and then the PCA method is used to reduce dimension of extracted features. The dictionary learning based on a single high resolution (HR) image is realized, which reduces the dependence of super-resolution algorithm on large datasets. The effectiveness of Gabor filter and double dictionary for restoration tool wear image details is verified by experiments. Compared with proposed method, traditional interpolation method, sparse coding super resolution model [32] and super resolution convolutional neural network model [26], the PSNR mean value of those methods are 43.62, 42.19, 43.20, 43.21, respectively, which shows that the proposed method has excellent performance in super-resolution reconstruction of micro milling tool images for tool wear monitoring.