Application of New Multi-Scale Edge Fusion Algorithm in Structural Edge Extraction of Aluminum Foam

Accurate extraction of structural edge information of aluminum foam is an important method to study the complex structural properties of aluminum foam, but the conventional single-scale edge detection method is difficult to achieve complete extraction of structural edge information of aluminum foam. However, the multi-scale fusion edge detection method based on Gaussian smoothing also has some problems, such as strong edge diffusion, weak edge degradation, edge pixel movement and so on. In order to solve the shortcomings of the above methods, this paper proposes a multi-scale edge fusion algorithm based on texture suppression, which can extract the edge information of aluminum foam structure more accurately and completely. Firstly, preprocess the image. The illumination component of the image is extracted by the multi-scale fusion method, and the luminance of the image is corrected by the adaptive luminance correction method based on the two-dimensional gamma function. Secondly, construct multi-scale space. It is proposed to construct the guiding image of the guiding filtering by using the bilateral texture filtering and construct the multi-scale space by changing the scale factor of the guiding filtering. Both bilateral filtering and guided filtering have the function of suppressing the texture information of the image while maintaining the structural edge features of the image. Finally, extract edge seeds and fuse multi-scale edges. A new multi-scale image edge fusion algorithm is proposed, which uses seed edges as a medium to gradually merge multi-scale image edges. In order to extract the edge information of the foam aluminum cross-section structure more accurately and completely, the algorithm further optimizes the edge using gradient direction consistency and non-maximum suppression. In order to verify the feasibility of the proposed algorithm, this paper uses the dataset to test the proposed algorithm and a variety of existing algorithms, and compare the results of various algorithms by quantitative analysis. The experimental results show that the proposed algorithm is feasible and effective, and its performance is better than the comparison algorithm.

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