Images from outdoor foggy scenes are degraded by atmosphere mediums. Due to scattering of atmospheric particles, degraded images lost contrasts and color fidelity, and appear poor visibility and blurred scene contents. Image dehazing has been concerned in computer and image processing applications, such as traffic surveillance and control system, topographic survey system, intelligent vehicles, outdoor objects detection and recognition. Based on the deeply analysis of the images formation process in the foggy weather, this paper proposes a new image degradation model incorporating burring in images caused by multiple scattering and implements images restoration with blurring removed. The main work includes: (1) Analyzed the image formation process, atmospheric scattering has different properties in various weather conditions. In dense fog and long distant imaging, blurring in images caused by multiple scattering could not be ignored. Based the mechanisms of single scattering and multiple scattering, a new foggy image degradation model has been proposed to interpret the image formation process in foggy weather. (2) Base on the model, the image restoration problem has been separated in two parts: removing airlight and deblurring. Single scattering has main influence on images in the process of removing airlight, then applying the MAP (maximum a posteriori) with the natural statistics prior and a soft matting regulation, the estimation of the transmission map could be generated through the iterative re-weighted least squares process. In the process of deblurring, since multiple scattering of atmospheric particles depends on the depth map, the blur kernel is modeled as the atmospheric point spread function, and then haze-free images are recovered using the MAP method. Results demonstrate that the new degradation model and algorithm could enhance visibility and remove blurring in images effectively, and achieve satisfying restoration effects For the designed embedded vision positioning system, many experiments are conducted, such as color block identification and segmentation, feature extraction, objects positioning, object approaching and obstacle avoidance for a mobile robot. The experimental results verify the effectiveness of the proposed embedded vision positioning system.
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