Robust polarization-based underwater image enhancement method using anchor brightness adaptation - Chen, Yuehan; Li, Yafeng; Wang, Yulin; Mi, Zetian; Wang, Yujia; Fu, Xianping (Optics and lasers in engineering, 2023)
•In previous studies, when estimating the DoP of background light, relying only on the AoP of the only pixel is vulnerable to interference from camera random noise. To solve this problem, we propose an effective neighborhood high fidelity constraint (NHFC) that can estimate the DoP of the background light more accurately and robustly.•method will be proposed to estimate the intensity of the background light at infinity using the limit idea, which provides an accurate and fast estimation of the most suitable background light area.•An anchor brightness adaptation algorithm will be proposed to eliminate the side effect of inaccurate exposure of the enhanced image, the process is effective and requires no interaction.•The experiments were conducted in total darkness to simulate the underwater nighttime optical imaging environment. Most of the polarization-based image enhancement methods do not take into account the inaccurate parameter estimation caused by the amplified camera noise at low illumination conditions. To solve this problem, this paper proposes a robust polarization-based underwater image enhancement method using anchor brightness adaptation (ABA). Relying on the relationship between the Stokes Vector and the angle of polarization (AoP), the proposed neighborhood high fidelity constraint (NHFC) can robustly select the region of the background light that is most suitable for parameter estimation, greatly reducing the interference of camera random noise under low illumination conditions and making full use of the characteristics of polarized optical imaging. The background light intensity at infinity and the scene transmission map are then estimated based on the polarization characteristics of the scene, thus effectively enhancing the image. Finally, ABA is introduced with the help of greyscale information of the original image to ensure the best exposure of the enhanced image. The experiments simulate underwater imaging at night, and the results show that the enhancement performance of this method is stable and can effectively solve the problem of being susceptible to random noise interference at low light, which has better applicability for real-time underwater image enhancement.