Decoupled variational retinex for reconstruction and fusion of underwater shallow depth-of-field image with parallax and moving objects

Abstract

Underwater imaging often suffers from poor quality due to the complex underwater environment and limitations of hardware equipment, leading to images with shallow depth of field and moving objects, which pose a challenge for information fusion of image sequences from the same underwater scene. To effectively address these problems, we propose a decoupled variational Retinex method for reconstructing and fusing underwater shallow depth of field images. Specifically, we first construct a module that adopts the decoupled variational Retinex model to adjust pixel dynamic range and luminance components, enhance non-local properties’ extraction with higher-order data constraints, and significantly improve image quality. Then, we develop a precision alignment strategy for image sequences by calculating and correcting control point deviations in the overlapping areas, achieving accurate registration of the image sequences, and effectively reconstructing scenes with parallax. Moreover, scenes with moving objects within the image sequence are reconstructed by redistributing overlapping areas. We design a novel cost function based on the neighborhood information of seams, which facilitates iterative optimization of these solved seams. This process improves the segmentation accuracy within these regions, achieving more precise scene reconstruction. Compared with state-of-the-art approaches, our method demonstrates superior performance in rectifying degraded image quality and reconstructing visually appealing images, with the resulting reconstructed images showing enhanced subjective visual quality.