Abstract

Image enhancement is a vital field in image processing that aims to improve the visual quality of images for optimal interpretation, analysis, and decision making. This paper presents the basic structure of deep learning networks, a comprehensive overview of state-of-the-art image enhancement deep learning techniques, the motivation behind selecting FPGAs as implementation processors, and an overview of hardware component improvements while addressing challenges to conventional approaches. Deep learning approaches, particularly convolutional neural networks (CNNs), generative adversarial networks (GANs), and autoencoders, have revolutionized the field by learning complex features and providing adaptive, data-driven enhancement solutions across medical imaging, satellite imagery, low-light photography, and video enhancement. With FPGAs offering flexible logic cells with accelerators to configure custom instruction sets, we explore the challenges in implementing deep learning methods, including computational complexity, real-time processing, and large dataset handling. Finally, we discuss emerging trends such as bringing mathematical approximators into deep learning algorithms for practical FPGA implementation.