In the current era, super-resolution has emerged as a prominent field of research aimed at enhancing the resolution and visual quality of low-resolution images. Generative Adversarial Networks (GANs) have demonstrated remarkable success in numerous computer vision domains, such as synthesis of images and restoration. In this work, we introduce an innovative approach for domain-specific super-resolution utilizing GANs. Our method focuses on leveraging the domain knowledge inherent in specific types of images to achieve superior results. In this work, we improve upon Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) and introduce domain-specific super-resolution networks that are trained using datasets of homogenous images. By adopting this approach, we effectively reduce the overall complexity associated with the super-resolution problem. Our findings demonstrate that the output of general-purpose networks lacks consistency. While these networks exhibit decent generalization capabilities for super-resolution tasks, their results often exhibit artifacts and subpar textures. Consequently, these models prove unsuitable for practical applications. Conversely, domain-specific models demonstrate high consistency within their trained problem domain. When comparing the outcomes of domain-specific models to the general-purpose ESRGAN model, we observe that the former achieves superior perceptual quality and texture, while exhibiting minimal to no artifacts. By emphasizing domain-specific features during training, our approach achieves super-resolution results which are enhanced and are tailored to the characteristics of the target domain. Experimental evaluations on various domain-specific datasets showcase the efficacy of our proposed approach. Our approach outperforms state-of-the-art super-resolution techniques in terms of both quantitative metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), as well as subjective visual quality. The results showcase the ability of our method to effectively enhance resolution and preserve domain-specific details in images.