In this paper, we propose a new single-image dehazing method. The proposed method constructs color ellipsoids that are statistically fitted to haze pixel clusters in RGB space and then calculates the transmission values through color ellipsoid geometry. The transmission values generated by the proposed method maximize the contrast of dehazed pixels, while preventing over-saturated pixels. The values are also statistically robust because they are calculated from the averages of the haze pixel values. Furthermore, rather than apply a highly complex refinement process to reduce halo or unnatural artifacts, we embed a fuzzy segmentation process into the construction of the color ellipsoid so that the proposed method simultaneously executes the transmission calculation and the refinement process. The results of an experimental performance evaluation verify that compared with prevailing dehazing methods the proposed method performs effectively across a wide range of haze and noise levels without causing any visible artifacts. Moreover, the relatively low complexity of the proposed method will facilitate its real-time applications.
In image dehazing, the existing transmission estimators bring out the halo artefact at boundaries unless they adopt a refinement procedure with a high computational complexity. How the existing transmission estimation methods suffer from the halo artefact at the boundaries are analysed and it is found that the elaborate, high computational refinement procedure to remove the halo effect are excessive for dehazing. On the basis of the above-mentioned analysis and finding, a simple segmentation logic was embedded in an existing transmission estimator, which is sufficiently accurate for dehazing. The experiment verifies that the proposed method significantly reduces the halo artefact without requiring any refinement procedure.
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