文章基本信息

标题：A Markov Random Field Model for the Restoration of Foggy Images
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作者：Fan Guo ; Jin Tang ; Hui Peng 等
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2014
卷号：11
DOI：10.5772/58674
语种：English
出版社：SAGE Publications
摘要：This paper presents an algorithm to remove fog from a single image using a Markov random field (MRF) framework. The method estimates the transmission map of an image degradation model by assigning labels with a MRF model and then optimizes the map estimation process using the graph cut-based α-expansion technique. The algorithm employs two steps. Initially, the transmission map is estimated using a dedicated MRF model combined with a bilateral filter. Next, the restored image is obtained by taking the estimated transmission map and the ambient light into the image degradation model to recover the scene radiance. The algorithm is controlled by just a few parameters that are automatically determined by a feedback mechanism. Results from a wide variety of synthetic and real foggy images demonstrate that the proposed method is effective and robust, yielding high-contrast and vivid defogging images. In addition to image defogging, surveillance video defogging based on a universal strategy and the application of a transmission map are also implemented.
关键词：Foggy Image; Defogging; Markov Random Field; Label Assignment; Transmission Map