In this paper, a novel method for image quality enhancement based on super-resolution algorithms in the frequency domain is presented. The proposed algorithm improves image quality by amplifying high-frequency components, which are crucial for preserving fine details and sharp edges. Unlike many existing super-resolution techniques that rely on multiple image frames, the proposed approach operates efficiently using only a single input frame. This characteristic not only simplifies implementation but also makes the method applicable in scenarios where acquiring multiple frames is impractical. A significant advantage of the proposed method is its reduced computational complexity compared to traditional super-resolution techniques, which often involve iterative optimization processes or deep learning models requiring extensive training datasets. By leveraging the frequency domain for enhancement, the algorithm achieves superior processing efficiency, making it particularly suitable for real-time applications such as video image processing. In such applications, computational speed is a critical factor, and the ability to enhance image quality without introducing excessive processing delays is highly desirable. To evaluate the effectiveness of the proposed method, extensive experiments were conducted on various image datasets, and the results demonstrate that the algorithm successfully enhances image sharpness while maintaining computational efficiency. The promising outcomes suggest potential applications in medical imaging, surveillance, and satellite image processing, where high-quality image reconstruction is essential.