Image field-of-view (FOV) enhancement is a promising technology that expands image visual scope and improves image visual effects, which has become a core research topic in the fields of image processing and computer vision. This technology has gained significant attention in both natural and underwater scenarios, demonstrating great application value across various domains. This paper surveys a comprehensive overview of image FOV enhancement, and these technologies are primarily classified via two avenues: fisheye image unwarping and image stitching, covering relevant approaches, system architectures, and future development directions in both natural and underwater scenarios. We summarize these existing researches and conduct an in-depth analysis of various methodologies, providing comprehensive and valuable references for future researchers. To distinguish from previous reviews, we provide a unified cross-domain comparison between natural and underwater scenarios, introduce a consistent classification framework that integrates both traditional and learning-based methods, and summarize system-level design insights that are significantly ignored in previous surveys. In addition, we analyze the critical challenges in image FOV enhancement, and explore potential directions for its future development.
Zhuang et al. (2026) studied this question.