MAP-regularized robust reconstruction for underwater imaging detection |
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Authors: | Yuzhang Chen Kecheng Yang |
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Affiliation: | 1. Faculty of Physics and Electronic Technology, Hubei University, Wuhan 430062, China;2. College of Optoelectronic Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China |
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Abstract: | In order to enhance the visual quality of underwater images, applications such as enhancement and restoration can be applied, but the resolution is still limited. Super-resolution reconstruction is a widely used technique for improving resolution beyond the limit of imaging system. With knowledge of the point spread function and techniques of regularization, the performance of reconstruction can be further enhanced. The presented effort proposed a robust image super-resolution reconstruction method under maximum a posteriori framework with regularization by the point spread function for underwater imaging detection. Objective image quality metrics are used to quantify the effectiveness of the reconstruction. Experimental results showed that the proposed method can effectively improve the resolution and quality of underwater imaging detection. |
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Keywords: | Information capacity MAP-regularized Point spread function Robust super-resolution reconstruction Underwater imaging detection |
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