<p>Rain is a typical meteorological phenomenon that can significantly impair the functionality of outdoor computer vision systems, including autonomous navigation and surveillance. Depending on how far away the streaks are from the camera, they may appear differently in the images. One input image serves as the foundation for the majority of current rain removal techniques. However, estimating a trustworthy depth map for rain removal is challenging on a single image. To overcome these challenges, this research introduces a novel approach for rain streak removal utilizing generative adversarial networks (GANs). Leveraging the discriminative power of GANs, the proposed technique effectively distinguishes between rain streaks and clean image content, resulting in the generation of realistic, rain-free images. The workflow involves initial image pre-processing using a cross-guided bilateral filter for detail layer extraction. The rain streak removal is then executed through an improved de-rain GAN (DR_GAN), where the generator module is replaced with a dense bidirectional network with self-attention (Attn_DBNet). This integration incorporates DenseNet-121, bidirectional gated recurrent unit (BiGRU), and self-attention mechanisms, enhancing the overall performance of the rain streak removal process. The research further introduces chaotic logistic gazelle optimization (CL-G) for optimizing the loss function, addressing local optimal trapping issues through the incorporation of chaotic logistic mapping. With notable gains in the metrics, comparative analysis shows that the proposed method is superior to the state-of-the-art approaches. These successes demonstrate the usefulness and superiority of the proposed GAN-based rain streak removal method over dual CNN, QSAM-Net, and MGPDNet approaches, with significant percentage advantages over these networks.&#xa0;</p>