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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Joint Image Restoration and Location in Visual Navigation System
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Joint Image Restoration and Location in Visual Navigation System

机译：视觉导航系统中的联合图像复原与定位

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摘要

Image location methods are the key technologies of visual navigation, most previous image location methods simply assume the ideal inputs without taking into account the real-world degradations (e.g. low resolution and blur). In view of such degradations, the conventional image location methods first perform image restoration and then match the restored image on the reference image. However, the defective output of the image restoration can affect the result of localization, by dealing with the restoration and location separately. In this paper, we present a joint image restoration and location (JRL) method, which utilizes the sparse representation prior to handle the challenging problem of low-quality image location. The sparse representation prior states that the degraded input image, if correctly restored, will have a good sparse representation in terms of the dictionary constructed from the reference image. By iteratively solving the image restoration in pursuit of the sparest representation, our method can achieve simultaneous restoration and location. Based on such a sparse representation prior, we demonstrate that the image restoration task and the location task can benefit greatly from each other. Extensive experiments on real scene images with Gaussian blur are carried out and our joint model outperforms the conventional methods of treating the two tasks independently.
机译：图像定位方法是视觉导航的关键技术，大多数以前的图像定位方法只是假定理想的输入，而没有考虑到现实世界中的降级（例如，低分辨率和模糊）。鉴于这种劣化，常规的图像定位方法首先执行图像恢复，然后将恢复的图像与参考图像匹配。但是，图像恢复的有缺陷输出可能会通过分别处理恢复和定位而影响定位的结果。在本文中，我们提出了一种联合图像复原和定位（JRL）方法，该方法利用稀疏表示来处理低质量图像定位这一具有挑战性的问题。稀疏表示先前表示，如果正确还原了降级的输入图像，则从参考图像构造的字典方面将具有良好的稀疏表示。通过迭代解决图像恢复以追求最大的表示，我们的方法可以实现同时恢复和定位。基于这种稀疏表示的先验，我们证明了图像恢复任务和定位任务可以彼此受益匪浅。在具有高斯模糊的真实场景图像上进行了广泛的实验，我们的联合模型优于独立处理这两项任务的常规方法。

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