Underwater Image Super-Resolution by Descattering and Fusion
Underwater Image Super-Resolution by Descattering and Fusion
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DOI：
10.1109/access.2017.2648845
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发表时间：
2017-01
期刊：
影响因子：
3.9
通讯作者：
Huimin Lu-;Yujie Li;Shota Nakashima;Hyongseop Kim;S. Serikawa
中科院分区：
文献类型：
--
作者：
Huimin Lu-;Yujie Li;Shota Nakashima;Hyongseop Kim;S. Serikawa
水下图像由于散射和吸收而退化，导致低对比度和颜色失真。提出了一种新的基于自相似的水下图像去散和超分辨率方法。传统的先使用散布算法对图像进行预处理，然后再应用随机共振方法的方法，存在着在解散射过程中丢失大部分高频信息的局限性。因此，我们提出了一种新的高浊度水下图像随机共聚焦算法。我们首先使用基于自相似的高分辨率(HR)算法获得散乱图像和散乱图像。接下来，我们应用一种凸融合规则来恢复最终的HR图像。超分辨率图像在去散布后具有合理的噪声水平，并显示出比传统方法更令人满意的视觉效果。此外，数值度量表明，该算法具有一致的改进效果，并且边缘得到显著增强。
Underwater images are degraded due to scatters and absorption, resulting in low contrast and color distortion. In this paper, a novel self-similarity-based method for descattering and super resolution (SR) of underwater images is proposed. The traditional approach of preprocessing the image using a descattering algorithm, followed by application of an SR method, has the limitation that most of the high-frequency information is lost during descattering. Consequently, we propose a novel high turbidity underwater image SR algorithm. We first obtain a high resolution (HR) image of scattered and descattered images by using a self-similarity-based SR algorithm. Next, we apply a convex fusion rule for recovering the final HR image. The super-resolved images have a reasonable noise level after descattering and demonstrate visually more pleasing results than conventional approaches. Furthermore, numerical metrics demonstrate that the proposed algorithm shows a consistent improvement and that edges are significantly enhanced.