Plankton is the base of the ocean ecosystem and is very sensitive to changes in their environment. Thus, monitoring the status of plankton in-situ has incredible importance for environmental study. Hologram is one of the most effective methods to record the plankton’s living status underwater. However, the reconstruction of holograms, conventionally achieved by numerical calculation, costs high both in computation and memory. Moreover, the plankton holograms are heavily noised, and useful information is sparsely distributed. To obtain high-speed visual image reconstruction from plankton holograms with good performance, in this paper, an efficient, low-redundant, and multi-object reconstruction network for plankton holograms, that is Holo-Net, is proposed. The Holo-Net includes a plankton detection unit and a reconstruction unit. It can first detect the plankton region and then map it to a visual image. A plankton hologram dataset is produced to verify the efficiency of the proposed method. Experiments show that the Holo-Net achieves PSNR and SSIM up to 20.61 and 0.65, respectively. More important, the Holo-Net is faster than the numerical method at least 100 times. We believe this work will facilitate the development of a compact in-situ plankton holographic monitoring system and help the research of the marine biosystem.
