Image Enhancement of Parts Surface Based on Illumination Correction and Image Fusion
The quality of surface images of machined parts collected under low illumination conditions is relatively poor,which significantly affects the subsequent extraction of roughness-related features.To this end,a low-light image enhancement algorithm based on illumination correction and image fusion is proposed.First,the guided filtering algorithm is improved to adaptively adjust the filtering parameters based on the texture of the image.This technique maintains the overall structure while smoothing the texture and obtains a higher quality illumination map.Then,inverse enhancement is performed on the original image to suppress the bright stripes and spots and integrate the high-quality pixels of the original image and the positive and negative enhanced images through image fusion.Subsequently,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm with limited contrast is used to enhance the contrast of the fused image.Finally,two different surface roughness detection models of parts are applied to the enhanced images.Experiments are conducted on a low-light image dataset of titanium,steel,and magnesium parts.The results show that the algorithm effectively enhances the quality of low-light part surface images,and the standard deviation,average gradient,and information entropy of the enhanced images are higher than those obtained with the existing algorithm.Compared with the results before image enhancement,the root mean square errors of the roughness detection models based on Gray Level Co-occurrence Matrix with Support Vector Regression(GLCM-SVR)and Regression Convolutional Neural Network(RCNN)are reduced by 0.140 and 0.202 μm,and the average absolute errors decrease by 0.116 and 0.146 μm,respectively.This indicates that image enhancement can effectively improve the accuracy of vision-based roughness detection methods under low illumination conditions.
万方数据
维普
