A Systematic Review of Wavelet-Based Pooling for Enhancing GANs: Central Trends, Auxiliary Pooling Strategies, and Complementary Wavelet Integrations

Generative Adversarial Networks (GANs) have revolutionized data synthesis across various domains, including medical image analysis, computer vision, and signal processing. Despite their success, GANs often face challenges such as training instability, mode collapse, and suboptimal image quality. Recent advancements have introduced wavelet transforms and specialized pooling techniques as architectural enhancements to address these issues. This systematic review primarily evaluates the impact of integrating wavelet-based pooling mechanisms into GAN architectures, as a novel approach to improving generative performance. In support of this central focus, we also examine other emerging pooling strategies as well as broader uses of wavelet transforms within GAN pipelines (e.g., in feature extraction or loss design). It compares these approaches against traditional GAN models and other generative frameworks to assess improvements in performance, stability, and computational efficiency. Following a comprehensive literature search across major databases, studies were selected based on predefined inclusion and exclusion criteria aligned with the PICO framework. The review focuses on GAN variants (e.g., DCGAN, StyleGAN, CycleGAN) incorporating wavelet-based decompositions, various pooling techniques (average, max, adaptive, and mixed), and hybrid approaches. Outcome measures include image quality metrics (FID, IS, PSNR, SSIM), training stability, feature preservation, and application-specific metrics such as diagnostic accuracy in medical image analysis. The findings indicate that wavelet and advanced pooling techniques contribute significantly to performance enhancements in GANs. Notable improvements include sharper image generation, better texture preservation, reduced mode collapse, and increased robustness to adversarial perturbations. In some cases, computational efficiency was also improved through multi-resolution representations. By foregrounding wavelet-based pooling and situating related techniques in context, this review provides a structured synthesis of architectural trends that enhance GAN performance across domains. These techniques provide architectural robustness and adaptability across diverse applications, warranting further exploration and standardization in future research.