Water scattering is a significant limiting factor for underwater imaging quality. It changes the transportation direction of the original light path, causes the attenuation of light intensity, and so on. In this work, we use a synthetic polarizing camera to capture the images with different polarization states and reduce the impact of water scattering in one step with the underwater light propagation model and the Stokes vector. In addition, an untrained deep network is designed to complete the image descattering processing. Compared with the methods based on deep learning or physical model prior, it is more efficient. This technology is suitable for use in portable underwater imaging optical systems for real-time imaging and detecting particulate matter such as microplastics and microbial particles. It also broadens the application of underwater polarization imaging.
We introduce a multi-branch model-based architecture for image reconstruction in lensless imaging. The structure consists of two learning branches, namely a physical model-based network, and a data-driven network. It uses intermediate outputs from the former as a prior for guiding the learning of the reconstruction neural network, which mimics the mapping between the reconstructed high-resolution images and raw images. We demonstrate that the proposed architecture offers a flexible combination of model-based methods and deep networks with superior reconstruction performance than methods using only an unrolled optimization network or pure deep neural networks for image reconstruction.
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