# An Optimized Genetic Algorithm-Based Wavelet Image Fusion Technique for PCB Detection
## 4 Experiment Results and Discussion
Figure 1: A case study of image fusion. (a) original1; (b) original2; (c) fused. Additionally, the proposed approach was further compared with commonly used image fusion methods, including the pixel averaging method and the Pulse Coupled Neural Network (PCNN) method [32; 33]. A set of representative examples illustrating the image fusion performance of the improved approach, the conventional method, the pixel averaging method, and the PCNN method is presented in Figure 2. Specifically, Figure 2a shows the fusion result obtained using the pixel averaging method, Figure 2b displays the result from the PCNN method, Figure 2c presents the fusion outcome of the conventional method before improvement, and Figure 2d demonstrates the fusion result achieved by the proposed approach. As observed in Figure 2, from a human visual perspective, Figure 2a, the pixel averaging method exhibits relatively poor performance. In Figure 2b, the distant object (flag) and the foreground object (person) in the PCNN method appear somewhat blurred compared to the proposed approach in Figure 2d. Similarly, in Figure 2c, the distant object (flag) in the conventional method appears less sharp than in Figure 2d. The proposed approach, as shown in Figure 2d, achieves clearer image fusion across all regions. Furthermore, a com  
Figure 2: Comparison of image fusion performance. (**a**) pixel; (**b**) PCNN; (**c**) tradition; (**d**) enhanced. -parative analysis of the fusion performance of the proposed method, the pixel averaging method, and the PCNN method across five sets of images is presented in Table 2.  
As shown in Table 3, the proposed approach demonstrates superior performance across multiple key metrics, particularly excelling in PSNR and image clarity, compared to the PCNN method. Additionally, it exhibits greater stability and sharpness than pixel-level fusion methods. The enhanced fusion approach effectively improves image quality while achieving a well-balanced performance across various aspects, ensuring the preservation of image details, sharpness, and overall quality. The proposed method shows a significant advantage in PSNR, with particularly high values in Test1-enhanced, whose PSNR equals 123.239, and Test5-enhanced, whose PSNR equals 120.192, where it notably outperforms both the PCNN and pixel-level methods. This indicates that the enhanced fusion method better retains image details and signal integrity while reducing noise introduced during the fusion process. A high PSNR value typically signifies superior image quality with minimal distortion. Furthermore, the proposed method consistently achieves an RSD value of approximately 1, indicating excellent stability and consistency in image fusion. While pixel-level methods tend to have lower RSD values, the enhanced method maintains a well-balanced performance through optimized algorithms, minimizing variations between images without compromising image quality. In terms of SF, the Test-enhanced method exhibits a moderate yet effective performance, particularly in Test3-enhanced, whose SF equals 477.058, and Test2-enhanced, whose SF equals 41.3933, demonstrating strong capability in preserving image details. Although its SF values are generally lower than those of pixel-level fusion methods, the proposed approach maintains clearer image structures