Underwater image restoration via 5 × 5 multi-view polarimetric sampling and fusion.

Abstract

Conventional monocular polarimetric imaging in turbid underwater environments suffers from severe particulate interference, leading to unreliable polarimetric parameter estimation and loss of image detail. This paper proposes AM-PDC, a multi-view polarimetric restoration framework based on sequential 5×5 spatial sampling using a single translated polarimetric camera. Feature alignment and pixel-level median fusion suppress stochastic noise, while multi-view variance characteristics enable automatic background extraction for estimating the background polarization direction. Backscatter suppression is performed through orthogonal polarization decomposition guided by the estimated background polarization direction, followed by contrast-truncated histogram stretching (CTHS) and contrast-limited adaptive histogram equalization (CLAHE) applied to the luminance channel. Experimental results demonstrate that AM-PDC recovers structural and textural information across varying turbidity levels and achieves improved structural preservation, visual quality, and detail enhancement under the evaluated medium-to-high turbidity conditions.