A Theoretical Framework for Establishing the Conditions for Implementing AI-Based Image Processing Methods in Photography

Artificial intelligence (AI) has become a fundamental component in modern image processing, significantly influencing the development of digital photography. Traditional image processing techniques rely on predefined mathematical models, which often fail to adapt to complex lighting conditions and diverse visual environments. In contrast, AI-based approaches utilize data-driven learning mechanisms capable of automatically extracting features and optimizing image quality. This paper presents a detailed analysis of artificial intelligence methods applied to photographic image processing, with a focus on convolutional neural networks (CNNs), generative adversarial networks (GANs), and deep enhancement models. The study examines their theoretical foundations, practical implementations, and experimental performance. Quantitative evaluation based on PSNR and SSIM metrics demonstrates the superiority of AI-driven approaches over classical methods. The results confirm that artificial intelligence plays a crucial role in modern photography and will continue to shape future developments in the field.