Anti-biofouling Lensless Camera System with Deep Learning based Image Reconstruction
<|reference_start|>Anti-biofouling Lensless Camera System with Deep Learning based Image Reconstruction: In recent years, there has been an increasing demand for underwater cameras that monitor the condition of offshore structures and check the number of individuals in aqua culture environments with long-period observation. One of the significant issues with this observation is that biofouling sticks to the aperture and lens densely and prevents cameras from capturing clear images. This study examines an underwater camera that applies material technologies with high inherent resistance to biofouling and computer vision technologies based on image reconstruction by deep learning to lens-less cameras. For this purpose, our prototype camera uses a coded aperture with 1k rectangular shape pinholes in a thin metal plate, such as copper, which hinder the growth of biofouling and keep the surface clean. Although images taken by lens-less cameras are usually not well formed due to lack of the traditional glass-based lens, a deep learning approach using ViT (Vision Transformer) has recently demonstrated reconstructing original photo images well and our study shows that using gated MLP (Multilayer Perceptron) also yields good results. On the other hand, a certain degree of thickness for bio-repellence materials is required to exhibit their effect the thickness of aperture is necessary to use apertures sufficiently thinner than the size of the pinholes to avoid unintentional reflection and absorption on the sidewalls. Therefore, we prepared a sufficiently thin plate for image reconstruction and now currently we conduct tests of the lens-less camera of the bio-repellence aperture with actual seawater environments to determine whether it can sufficiently demonstrate the biofouling effect compared with usual camera with only waterproof.<|reference_end|>
@article{ide2024anti-biofouling,
    title={Anti-biofouling Lensless Camera System with Deep Learning based Image
  Reconstruction},
    author={Naoki Ide, Tomohiro Kawahara, Hiroshi Ueno, Daiki Yanagidaira, and
  Susumu Takatsuka},
    journal={arXiv preprint arXiv:2410.01365},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01365},
    primaryClass={eess.IV cs.CV}
}