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Haze removal is an important image restoration technology that aims to remove annoying haze particles from images. However, the efficacies of traditional dehazing methods are easily hindered by insufficient estimation of haze thickness, and thus cannot effectively provide satisfactory haze removal results. In this paper, we propose an edge collapse-based dehazing algorithm by which to dynamically...
Images in fog or smog degrade dreadfully, although there were quite a few image dehazing methods, which were not very effective to smog images. This paper proposed a novel dehazing method based on propagating deconvolution and dark-channel prior. Propagating deconvolution aimed at recovering smog image to get rid of the smog in front of the scene, it could transit the "smog image" to "fog...
Images of roads captured in bad weather suffer from poor contrasts and colors. It is known that the degradation of an image due to scattering of light is exponential in the depths of the scene points. Therefore, to estimate the depths, previous algorithms using physical model develop an interactive step. But the method is limited, because it needs to estimate four or more unknown parameters and select...
This paper presents a technique for image segmentation. We demonstrate its efficacy for classsifying high-resolution aerial images. The application is peak water flow estimation in a river catchment in the city of Zurich and the data covers a large rural and urban setting. The output of the segmentation process is used as input to a hydrological model. We introduce a combined, probabilistic, segmentation...
Images captured under bad weather conditions usually have poor contrasts and colors. Due to the scattering of light, the degradation of an image increases exponentially with the depths of the scene points. Previously implemented methods are limited, because an interactive step was required to select the sky brightness and the vanishing point of the image, as well as the information about the atmospheric...
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