Construction of Digital Image Segmentation Automation Processing System Based on Improved Firefly Algorithm

With the rapid development of information technology and digital technology, the generation of massive image data has put forward higher requirements for image processing technology. Image segmentation, as an important step in image processing, also faces huge challenges. Therefore, this study improved the firefly algorithm by integrating adaptive step size, covariance elite selection, and neighborhood search scheme, and constructed a grayscale threshold image segmentation model based on the improved algorithm. The test results showed that the Jacobian values of the proposed model at thresholds of 2, 3, 4, and 5 were 0.907, 0.919, 0.946, and 0.957, respectively, and the Dice coefficients were 0.9187, 0.951, 0.9617, and 0.9586, respectively. After image segmentation, the optimal peak signal-to-noise ratio and structural similarity index were 22.8462 and 0.76281, respectively. The experimental results show that the research can effectively improve the accuracy and edge preservation ability of image segmentation by combining the improved swarm intelligence algorithm with grayscale threshold segmentation technology, providing new technical means and solutions for the field of image segmentation, and has certain practical application value.