Artificial Intelligence (AI) has redefined long-standing challenges in image processing, particularly in defogging and low-light image enhancement. The inherent flexibility and adaptability of AI technologies have significantly advanced enhancement outcomes. However, deploying state-of-the-art image enhancement models on low-power devices while maintaining real-time processing capabilities remains a critical challenge. This paper introduces a novel AI model architecture with approximately 4K parameters, developed using deep learning techniques and grounded in well-established principles such as the Atmospheric Scattering Model, Dark Channel Prior, and Local Maximum Color Value Prior. The proposed model comprises three key components: a transmission map estimation module, a color correction module,and a denoising module. Inspired by advanced concepts such as Residual Networks and Feature Pyramid Networks, the architecture achieves an optimal balance between computational efficiency and enhancement performance. Our model demonstrates competitive results in both low-light image enhancement and defogging tasks, achieving a Peak Signal-to-Noise Ratio (PSNR) exceeding 20. Furthermore, it processes 1280x720 high-definition (HD) images at an impressive speed of 0.03 seconds per frame. This remarkable processing efficiency makes the model highly suitable for deployment in high-performance, resource-constrained systems, such as home surveillance equipment and autonomous vehicle platforms.