Underwater imaging plays an important role in marine geological exploitation, resource exploitation, ecological research and many other fields. Due to the influence of complex underwater environment, the underwater images have some degradation problems, such as low contrast, high scattering noise and so on. Therefore, the improvement of underwater imaging quality is of great significance in any actual application. In this paper, an imaging experiment for the underwater targets is performed by utilizing a time-of-flight (TOF) camera combining with an underwater physical imaging model and an image enhancement method, where the advantage of the TOF camera directly obtaining the depth map can be full taken to calculate the unknown parameters in the underwater physical imaging model. The results show that the better TOF underwater images can be achieved, which can be verified in the feature point matching. Compared with other imaging systems, the TOF underwater imaging method proposed here has much better characteristics in high anti-jamming capability, high sensitivity, all-weather operation and so on.
The image reconstruction of objects that are hidden from a camera’s view is a challenging issue of imaging in many fields of research due to its many potential applications in robotic vision , remote sensing and autonomous vehicles and so on. However, most of non-line-of -sight (NLOS) imaging experiments were demonstrated through spatially scanning a visible target surface with a pulsed laser and a time resolved detector, where both the system and image reconstruction are complicated. Here, we performed a transient non-line-of-sight imaging experiment by using a time-of-flight (TOF) camera, in which the images can be recovered via just capturing the light transport information encoded in multiply scattered light in a short time of 8.3 ms. It also has some advantages of lower 8W power requirements, lower cost, and shorter reconstruction time compared to the previous systems. Additionally, we propose a method of data processing based on Gaussian fitting to reduce the noises of images, where image quality can significantly improved.
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