Underwater Image Enhancement Method Based on Polarized Images Fusion and Quality Evaluation

Underwater images suffer from blur, low contrast, color distortion and loss of details. Recently, since polarized images can provide more information, many underwater image enhancement(UIE) methods based on polarized images are proposed. Most existing polarized methods usually enhance images by physical techniques based on some priors or assumptions. However, the priors or assumptions are not always reliable in the real scene, which limits their performance. Besides, existing deep learning methods fuse four polarized images at different angles (I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0–3</inf>) into a clear visible light image, which only use simple fusion methods and do not preserve image information well. In order to avoid the limitation of priors and obtain the rich information contained in polarized images more reasonable, we propose a novel framework that using the quality maps to guide the fusion of the four images to enhance images, called Quality-Guided Enhancement Network(QGE-Net). Specifically, QGE-Net can be divided into Quality Evaluator and Enhancement Module. A loss called SROCC Rank Loss is proposed to train Quality Evaluator, which can improve the ability of Quality Evaluator to obtain differential information from different images in the same scene. In Enhancement Module, a module called Multi-Scale Quality Guidance Fusion(MQGF) is proposed to fuse the features of polarized images under the guidance of the quality map to make full use of the information of the input image. Extensive experimental results show that the proposed method achieves superior performance against state-of-the-art methods on both synthetic and real data.