Published Paper Details:

Image registration is a fundamental process in computer vision and medical imaging that involves aligning two or more images into a common coordinate framework. It is widely applied in fields such as remote sensing, medical diagnostics, and robotics. Traditional image registration methods rely on optimization techniques and similarity metrics, but they often face challenges such as computational complexity and sensitivity to noise. Machine learning techniques, particularly deep learning, have emerged as powerful tools to overcome these limitations. This review provides a comprehensive analysis of machine learning methods used for image registration, emphasizing their architectures, optimization strategies, and performance metrics. We explore supervised, unsupervised, and reinforcement learning approaches, highlighting their strengths and limitations. Furthermore, the paper discusses key advancements in neural network designs, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, which have demonstrated remarkable performance in handling large datasets and complex transformations. The literature review includes studies published between 2018 and 2024, covering theoretical foundations, algorithmic implementations, and practical applications. The paper concludes by identifying open challenges and future research directions to further advance image registration techniques.