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Efficient image dehazing using multi-objective differential evolution

机译:使用多目标差分演进的高效图像脱水

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Digital image haze removal algorithms become more valuable for most vision applications. It is found that majority of researchers has ignored lots of challenges such as the problem of halo artifacts, edge preservation, color distortion, etc. To get rid of the issues with previous research, the latest integrated algorithm is proposed. The proposed technique has modified well-known dark channel prior based dehazing technique by using the multi-objective optimization fitness function. Adaptive histogram equalization has also been used to remove the saturated pixel issue of the digital image haze removal. The proposed technique is tested on five well-known hazy images. Extensive analysis has shown that the proposed method can remove the limitations of existing methods.
机译:数字图像雾霾去除算法对大多数视觉应用变得更有价值。结果发现,大多数研究人员忽略了许多挑战,如晕术伪影,边缘保存,颜色失真等问题,以摆脱以前的研究,提出了最新的集成算法。通过使用多目标优化健身功能,所提出的技术通过多目标优化健身功能修改了众所周知的暗信道先前的脱水技术。自适应直方图均衡也已用于去除数字图像雾化的饱和像素问题。在五个众所周知的朦胧图像上测试了所提出的技术。广泛的分析表明,该方法可以消除现有方法的局限性。

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