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Images captured in bad weather condition suffer from the adverse effects of atmosphere. In these conditions the light going through the atmosphere is dimmed, which results in the decrease in the quality of the images. Although the traditional methods address the problem of poor visibility, they can not reach to an acceptable result in terms of quality and complexity. In this paper, a new method is...
Outdoor photography and computer vision tasks often suffer from bad weather conditions, observed objects lose visibility and contrast due to the presence of atmospheric haze, fog, and smoke. In this paper, we propose a new method for real-time image and video dehazing. Based on a newly presented haze-free image prior - dark channel prior and a common haze imaging model, for a single input image, we...
It is well known that depth can be recovered from a single image using defogging method. Inspiriting by the idea of defogging using dark channel prior, we propose a new approach to recover depth. The input is a foggy image. We defog the image using an improved dark channel prior method. We adopt the dilating and eroding method to refine the block effect. Experimental results show that our method can...
Sparse signal representations and approximations from overcomplete dictionaries have become an invaluable tool recently. In this paper, we develop a new, heuristic, graph-structured, sparse signal representation algorithm for overcomplete dictionaries that can be decomposed into subdictionaries and whose dictionary elements can be arranged in a hierarchy. Around this algorithm, we construct a methodology...
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