Generative Adversarial Networks (GANs) have risen to prominence in the field of deep learning, facilitating the generation of realistic data from random noise. The effectiveness of GANs often depends on the quality of feature extraction, a critical aspect of their architecture. This paper introduces L-WaveBlock, a novel and robust feature extractor that leverages the capabilities of the Discrete Wavelet Transform (DWT) with deep learning methodologies. L-WaveBlock is catered to quicken the convergence of GAN generators while simultaneously enhancing their performance. The paper demonstrates the remarkable utility of L-WaveBlock across three datasets, a road satellite imagery dataset, the CelebA dataset and the GoPro dataset, showcasing its ability to ease feature extraction and make it more efficient. By utilizing DWT, L-WaveBlock efficiently captures the intricate details of both structural and textural details, and further partitions feature maps into orthogonal subbands across multiple scales while preserving essential information at the same time. Not only does it lead to faster convergence, but also gives competent results on every dataset by employing the L-WaveBlock. The proposed method achieves an Inception Score of 3.6959 and a Structural Similarity Index of 0.4261 on the maps dataset, a Peak Signal-to-Noise Ratio of 29.05 and a Structural Similarity Index of 0.874 on the CelebA dataset. The proposed method performs competently to the state-of-the-art for the image denoising dataset, albeit not better, but still leads to faster convergence than conventional methods. With this, L-WaveBlock emerges as a robust and efficient tool for enhancing GAN-based image generation, demonstrating superior convergence speed and competitive performance across multiple datasets for image resolution, image generation and image denoising.

翻译：生成对抗网络（GANs）在深度学习领域已崭露头角，能够从随机噪声中生成逼真数据。GAN的有效性通常取决于其特征提取的质量，这是其架构的一个关键方面。本文提出L-WaveBlock，一种新颖且鲁棒的特征提取器，它将离散小波变换（DWT）的能力与深度学习方法相结合。L-WaveBlock旨在加速GAN生成器的收敛，同时提升其性能。本文在三个数据集（道路卫星图像数据集、CelebA数据集和GoPro数据集）上展示了L-WaveBlock的显著效用，证明了其简化并提升特征提取效率的能力。通过利用DWT，L-WaveBlock高效地捕捉了结构细节和纹理细节，并将特征图跨多个尺度进一步划分为正交子带，同时保留了关键信息。它不仅实现了更快的收敛，而且通过应用L-WaveBlock在每个数据集上都取得了优异的结果。所提方法在maps数据集上取得了3.6959的Inception评分和0.4261的结构相似性指数，在CelebA数据集上取得了29.05的峰值信噪比和0.874的结构相似性指数。在图像去噪数据集上，所提方法表现与当前最先进技术相当，虽未超越，但仍比传统方法收敛更快。由此，L-WaveBlock作为一种增强基于GAN的图像生成的鲁棒且高效的工具，在图像分辨率、图像生成和图像去噪等多个数据集上展现了卓越的收敛速度和有竞争力的性能。