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首页> 外文会议>ACM/IEEE international conference on information processing in sensor networks >Poster Abstract: Image Sensing under Unfavorable Photographic Conditions with a Group of Wireless Image Sensors
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Poster Abstract: Image Sensing under Unfavorable Photographic Conditions with a Group of Wireless Image Sensors

机译：海报摘要：图像感应在不利的摄影条件下，带有一组无线图像传感器

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摘要

We investigate the problem of image sensing under unfavorable photographic conditions in a wireless image sensor network. In the scenes with deflective and/or reflective medium such as fogs, mirrors, glasses, degraded images are captured by those image sensors. Such degraded images often lack perceptual vividness and they offer a poor visibility of the scene contents. Notably, computation-intensive method to recover a better image based on single image [2] may not be applicable for wireless image sensors due to the limited computation capacities and the limited power resources (batteries) typically equipped at those wireless image sensors. In this paper, we propose a framework to recover better images under unfavorable photographic conditions in a wireless image sensor network, where an efficient decision fusion approach based on the reinforcement learning technique to infer the presence of an unfavorable photographic condition is achieved among image sensors on the fly and a subsequent light-weighted computation method based on multiple images is employed to recover better images. The preliminary results show the effectiveness of the proposed framework.
机译：我们在无线图像传感器网络中调查在不利的摄影条件下的图像感测的问题。在具有偏转和/或反射介质的场景中，例如雾，镜子，眼镜，可通过那些图像传感器捕获降级图像。这种降级的图像经常缺乏感知的鲜艳感知，并且它们提供了较差的现场内容的可见性。值得注意的是，基于单个图像恢复更好的图像的计算密集型方法可能不适用于由于有限的计算能力和通常配备在那些无线图像传感器的有限电力资源（电池）的无线图像传感器。在本文中，我们提出了一种在无线图像传感器网络中在不利的摄影条件下恢复更好的图像的框架，其中基于增强学习技术推断出不利的摄影条件的存在的有效决策融合方法是在图像传感器之间进行的用于基于多个图像的飞行和随后的光加权计算方法来恢复更好的图像。初步结果表明了拟议框架的有效性。

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