Strip steel defect image enhancement based on wavelet denoising and homomorphic filtering
LI Heng
CUI Ying
ZHAO Lei
LIU Hui
Abstract：[Objective]The iron and steel industry,as one of the pillar industries of economic development in China,has an irreplaceable position in the entire manufacturing industry.Hot rolled strip steel has the advantages of strong covering capacity,easy processing,and material saving and is the raw material for producing other steel products.Improving the surface quality of strip steel products is an important part of improving the quality of steel products.Due to the influence of many factors of production,processing,shooting,etc.,the brightness of the surface defect image of the original strip steel is uneven,and the contrast between the defect area and the non-defect area is low.As a result,the defect information is not clear enough for easy detection.To solve the above problems,a method for surface defect image enhancement of strip steel based on wavelet denoising and improved homomorphic filtering was proposed.[Methods]The original image was decomposed into low-frequency component and high-frequency component by two-level wavelet transform.The low-frequency component contained the main information of the original image,which was enhanced to improve the overall effect of the image.The improved homomorphic filtering algorithm and the contrast limited adaptive histogram equalization(CLAHE)algorithm were used to enhance the low-frequency component,equalizing the image brightness and improving the overall contrast.Moreover,the low-frequency images after being processed by the above two algorithms were fused with appropriate weights to obtain the enhanced low-frequency component.The high-frequency component contained the detail information of the image and noise.The improved threshold function was used to improve the denoising effect of the high-frequency component,and the edge details were well preserved.Finally,the processed low-frequency and high-frequency components were subjected to wavelet reconstruction to obtain the final enhanced image.[Results]Multiple sets of comparative analysis were conducted on the processing results of the algorithm through subjective visual evaluation and objective evaluation indicators.Brightness is significantly improved for all kinds of surface defect images of strip steel which are enhanced by the proposed algorithm compared with other algorithms,and the overall brightness remains balanced.At the same time,the contrast is improved,and the texture details and defect information of the images are more obvious.The mean square error(MSE),peak signal-to-noise ratio(PSNR),and image entropy(IE)were used for evaluating the algorithms,and each parameter was comprehensively analyzed.The proposed algorithm has a remarkable effect on improving contrast and reducing noise.It retains more details and leads to less distortion.[Conclusion]Experimental results show that the proposed algorithm can effectively enhance the brightness evenness and overall contrast of surface defect images of strip steel,strengthen the denoising effect,and significantly enhance defect information and edge details.It is suitable for the detection of various types of strip steel surface defects.
Keywords：wavelet transformhomomorphic filteringthreshold denoisingimage enhancementstrip steelsurface defectcontrast limited adaptive histogram equalizationwavelet reconstruction
Publication Date：2025-05-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 369-376 )