The challenges associated with acquiring the clear images of objects in underwater environment are difficult to overcome due to the absorptive and scattering nature of seawater. Recently, the research community has focused on mitigating these effects. The recent developments in image enhancement algorithms and strategies of signal light enhancement have brought improvement in some application areas. In this work, we review the six most common methods based on signal light enhancement. We present the individual working mechanisms, latest representative advances, and suitable application conditions. Moreover, we also present a detailed comparison of these techniques. In each technique, we present their applicable environments and conditions according to the following indicators: operating distance (from 2 attenuation lengths (AL) to 13.5 AL), resolution (from centimeter to millimeter), and field of view (FOV). By summarizing and analyzing the existing problems that restrict the underwater optical imaging techniques, the future development trends are prospected.

Polarization imaging has the advantage of detecting artificial targets based on their intrinsic characteristics. However, with the development of camouflage materials and camouflage shielding performance, the anti-optical detection technology for camouflaged targets continues to improve. In this paper, we combine the advantages of polarization imaging and deep learning to achieve rapid detection of artificial targets camouflaged in natural scenes. Firstly, we propose a Stokes-vector-based parameter image to show the polarization specificity of the camouflaged artificial targets. Then, a detection method is proposed, which uses an Otsu segmentation algorithm and morphological operations to extract polarization signatures of the target from the proposed parameter image, and utilizes the extracted polarization signatures to highlight the camouflaged artificial targets. Finally, we improve a self-supervised deep learning network to enhance the low-light images, extending the application of our method into low illumination environment target detection. Experimental results demonstrate that our method can effectively detect the camouflaged artificial targets with a detection rate better than 80%, which has potential application value in the fields of military target detection, security monitoring, and remote sensing.