Image enhancement is to improve the image definition. Unlike the original image, image enhancement does not need to completely reflect the original image, but displays some distortion more clearly. The purpose of image enhancement is to better improve the visual impact of the image. In a special area, the salient features of the target will make the unclear image more clear, or the characteristics of the favorite part will be processed clearly, expand the difference between this part of features and other objects, suppress the uninterested areas, improve the image quality of the interested areas, and thus enrich the data volume of the image, The image recognition technology has a good effect and can meet the needs of image processing.
After filtering and denoising the spur gear defect image, the image needs to be further processed to enhance the visual quality of the spur gear defect, and the gray level segmented linear transformation enhancement is selected to process the denoised spur gear defect image, as shown in the figure.
Different image processing optimization algorithms have different characteristics. When using different algorithms to enhance the same spur gear defect image, the enhancement effect is different. Data analysis is carried out according to three optimization algorithms: histogram equalization, gray-scale piecewise linear transformation and gray-scale linear transformation. The results show that the gray value distribution of the image after gray-scale piecewise linear transformation is more uniform, and the defect area of the spur gear surface is enhanced, And the details in the spur gear defect image are protected. Therefore, this paper first uses the improved adaptive median filter algorithm to denoise the image, and then uses the piecewise linear transformation to enhance the denoised spur gear defect image.
