AI-Driven Intelligent Image Processing: a Novel Framework for Real-Time Image Enhancement and Analysis

AI-powered smart image processing has transformed real-time image enhancement and analysis by greatly enhancing image quality, object detection accuracy, and computational efficiency. This paper introduces an extensive framework comparing CNN-based, GAN-based, and Transformer-based models with conventional image processing methods. The comparison is made using performance metrics like PSNR, SSIM, MSE, processing time, and computational efficiency. Results show that deep learning algorithms, especially transformers and GANs, outshine traditional methods in image quality, feature detection, and accuracy of edge detection. In addition, this work emphasizes the compromise between model size and real-time processing speed and the importance of using lightweight AI structures for deployment at the edge. Directions in the future include minimizing AI models to fit low-power devices, bringing hybrid deep learning frameworks on board, and harnessing selfsupervised learning to maximize adaptability and scalability. Improvements in hardware acceleration (FPGAs, TPUs, and GPUs) will enhance computational efficiency, making it viable for real-time applications in autonomous systems, medical imaging, and surveillance. Efforts in ensuring ethical compliance and security requirements of AI-driven image processing remain necessary to deliver dependable and nondiscriminative AI solutions. This work helps advance the nextgeneration intelligent vision systems towards enabling more efficient, scalable, and intelligent real-time image enhancement technology.