Review of Image Enhancement Techniques and FPGA-Based Hardware Implementations

Image enhancement is a vital field in image processing; that aims to improve the visual quality of images for best interpretation, analysis, and decision making. This paper presents the basic structure of deep learning networks, comprehensive overview of the state-of-the-art image enhancement deep learning technique, motivation behind the selection of FPGA as implementation processor, and an overview of hardware component improvements; also presenting challenges to conventional approaches and the solutions to them. 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. The review highlights the effectiveness of deep learning approaches in various application domains, such as medical imaging, satellite imagery, low-light photography, and video enhancement. With FPGA offering offering basic logic cells with accelerators like LUTs, DSPs etc. to configure any instruction set; making it a favorable co-processor (truly unlimited processor because it can be configured to perform any instruction); We further explore the challenges in implementing deep learning methods, such as computational complexity, real-time processing, and the need to process large datasets. Finally, we discuss emerging trends and future directions in the field such as bringing in published approximators to models used in deep learning algorithms to make them implementable on FPGA. This review aims to provide a valuable resource for researchers and practitioners seeking to understand the landscape of image enhancement methods and their practical implementation.