# An Optimized Genetic Algorithm-Based Wavelet Image Fusion Technique for PCB Detection
## 2 Related Work  
Image fusion has become a pivotal technique for combining complementary information from multiple image sources, enhancing visual interpretation and decision-making in fields such as medical diagnosis, remote sensing, and surveillance. [PERSON], [PERSON] et al. present a competitive mask-guidance fusion method for infrared and visible images, utilizing a multimodal semantic-sensitive mask selection network and a bidirectional-collaboration region fusion strategy to effectively integrate advantageous target regions from different modalities, significantly improving the saliency and structural integrity of fused images [7]. [PERSON], [PERSON] et al. introduce FusionBooster, a model designed for image fusion tasks, which improves fusion performance through a divide-and-conquer strategy controlled by an information probe, consisting of probe units, a booster layer, and an assembling module, with experimental results showing significant improvements in fusion and downstream detection tasks [8]. [PERSON], [PERSON], [PERSON] and [PERSON] present a novel infrared and visible image fusion method that leverages a multimodal large language model with CLIP-driven Information Injection (CII) and CLIP-guided Feature Fusion (CFF) strategies to enhance image quality and address complex scene challenges without relying on complex network structures [9]. ReFusion is a meta-learning-based image fusion framework that dynamically optimizes fusion loss through source image reconstruction, employing a parameterized loss function and a three-module structure to adapt to various fusion tasks and consistently achieve superior results [10]. Other contributions in the field include a wavelet-based medical image fusion method proposed by [PERSON] et al., which combines human visual system characteristics with wavelet coefficients' physical meaning [11]. Furthermore, [PERSON] et al. present a wavelet-based multi-focus image fusion algorithm that combines SSIM and edge energy matrices to select high-frequency coefficients, improving fusion quality by reducing ringing effects and information loss [12]. Similarly, [PERSON] proposes a wavelet-based image fusion technique using dual-tree complex wavelet transform to preserve local perceptual features, thus avoiding ghosting, aliasing, and haloing when combining images captured from different instruments [13]. An advanced wavelet-based fusion method, proposed by [PERSON] and [PERSON], integrates wavelet transform with a Fourier random measurement matrix [14]. This approach reduces time complexity while improving performance through compressive sensing sampling. In the domain of multi-focus image fusion, [PERSON] et al. utilize a dual-treecomplex wavelet-based framework that improves fusion accuracy through contrast-based initial fusion and decision-map-guided final fusion [15]. In the field of wavelet-based image fusion, [PERSON] et al. introduce a fusion algorithm integrating histogram equalization and sharpening techniques to enhance degraded images by improving contrast and detail clarity [16]. [PERSON] et al. present a wavelet-based multi-focus image fusion method using method noise and anisotropic diffusion, which enhances real-time surveillance images by improving clarity and visual quality [17]. Additionally, [PERSON] et al. propose a wavelet-based multi-focus image fusion method that optimizes image quality using genetic algorithms and HVS-based weighting to ensure enhanced perceptual clarity [18]. A wavelet-based multi-view fusion method enhances LSFM imaging by integrating directional information, improving contrast and detail without requiring PSF knowledge [19]. Several studies have also explored the integration of GA into wavelet-based fusion to further optimize fusion processes [20,21]. The integration of GAs into wavelet-based methods has further optimized fusion processes by dynamically selecting optimal fusion rules and parameters [22, 23, 24].