A Markov Random Field Model for the Restoration of Foggy Images

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.

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