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 article 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.

Underwater light field (ULF) imaging has emerged as a promising technology for capturing a more comprehensive array of visual information from real-world underwater environments. Unlike conventional underwater photography, which captures a two-dimensional projection of light that integrates over the angular domain, ULF imaging collects radiance from multiple directions. This enables the recovery of angular information that is typically lost in traditional imaging methods. Although ULF presents high-dimensional challenges such as inaccurate modeling and complex feature extraction, its ability to represent underwater visual data enhances the understanding of marine scenes. This, in turn, significantly improves the performance of various underwater vision tasks. The field of ULF imaging has garnered increasing attention in both the computer vision and computer graphics communities. This paper presents a comprehensive overview of the research conducted in this area over the past two decades. We focus on various aspects of ULF imaging, including ULF models, theory progression, parameter calibration, external and internal influencing factors, underwater scattering and refraction removal, underwater image enhancement/restoration, expansion of underwater imaging distance, underwater object detection, and underwater 3D reconstruction. Additionally, we analyze the current challenges facing ULF imaging technology and explore potential directions for its future development.