## 2 Background and Related Work
Nonlinear coupling methods can surpass the diffraction limit but require sophisticated setups, high-power ultrasound sources, and precise calibration, making their implementation challenging in practical industrial settings. These conventional methods often face difficulties in balancing resolution enhancement and processing efficiency, posing constraints for high-throughput inspections where rapid and reliable imaging is essential. Recently, deep learning-based techniques have emerged as highly promising alternatives, showcasing significant potential for advancement [14, 15, 16]. [PERSON] et al. introduced a deep learning-based method to achieve HR imaging of 17 distinct coin types using acoustic microscopy [17]. [PERSON] et al. introduced HDL-SAM, a hybrid deep learning framework that integrates SwinIR and U-Net architectures to enhance the resolution of acoustic images [18]. This approach effectively improves the signal-to-noise ratio, accommodates variations in scanning step size, and optimizes transducer frequency, enabling HR imaging. [PERSON] [PERSON] et al. conducted a comparative analysis of two conventional deconvolution-based techniques and a deep learning-based method for resolution enhancement of SAM images [19]. The study demonstrated that, despite utilizing a relatively limited training dataset, the deep learning approach substantially surpassed the performance of the classical deconvolution methods. Deep learning-based approaches for acoustic image SR have increasingly used GAN techniques, enabling convolutional neural networks (CNNs) to effectively extract feature representations from intricate data distributions [20]. A GAN architecture comprises two components: a generator network and a discriminator network. The generator network is designed to map random input vectors to a defined image space, while the discriminator is tasked with distinguishing between authentic images and those generated by the model. [PERSON] et al. integrated GANs with hypergraph-based techniques to achieve significant improvements in SAM image resolution and quality [21]. [PERSON] et al. proposed a methodology involving the training of multiple GAN architectures to perform acoustic image inpainting, effectively filling missing regions in the original images and generating a 4\(\times\) super-resolved output [22]. To address the challenge of limited datasets, [PERSON] et al. introduced a GAN framework enhanced with a residual attention module, designed to enhance the quality and detail of SAM images for flip-chip samples, resulting in an enriched dataset with improved image quality and quantity [23]. In this study, we leveraged the advantages of the GAN architecture to develop an enhanced model, LinearTGAN, specifically designed for linear acoustic imaging in wafer inspection applications. A comprehensive evaluation of our LinearTGAN was performed using four distinct metrics, and its performance was compared to multiple state-of-the-art (SOTA) SR models. This comprehensive analysis provides an objective assessment and effectively demonstrates the efficacy of our model in acoustic SR imaging tasks for wafer inspection in industrial applications.