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A fast image dehazing algorithm based on negative correction

机译:基于负校正的快速图像去雾算法

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摘要

Dehazing is an important but difficult issue for image processing. Recently, many dehazing algorithms have been proposed based on the dark channel prior. However, these algorithms fail to achieve a good tradeoff between the dehazing performance and the computational complexity. Moreover, the perceptual quality of these algorithms can be further improved, especially for sky areas. Therefore, this paper firstly introduces the concept of negative correction inspired by the practical application of photographic developing and a fast image dehazing algorithm is accordingly proposed. Based on the observation of the photographic developing, we find that the contrast of images can be enlarged and their saturation can also be increased when their negative images (or reverse image) are rectified. Thus, instead of estimating the transmission map, the correction factor of negative is estimated and it is used to rectify the corresponding haze images. In order to suppress halos, a modified maximum-filter is proposed to limit the larger value of correction factor of local region. The experimental results demonstrate that the proposed algorithm can effectively remove hazes and maintain the naturalness of images. Moreover, the proposed algorithm can significantly reduce the computational complexity by 56.14% on average when compared with the state-of-the-art.
机译:除雾是图像处理的重要但困难的问题。近来,已经基于暗信道先验提出了许多去雾算法。但是,这些算法无法在除雾性能和计算复杂度之间取得良好的折衷。此外,这些算法的感知质量可以进一步提高,尤其是在天空区域。因此,本文首先介绍了负片校正的概念,其灵感来自于照相显影的实际应用,并据此提出了一种快速图像去雾算法。基于对摄影显影的观察,我们发现当校正其负像(或反像)时,图像的对比度可以扩大,饱和度也可以提高。因此,不是估计透射图,而是估计负的校正因子,并将其用于校正相应的雾度图像。为了抑制光晕,提出了一种改进的最大滤波器来限制局部区域校正因子的较大值。实验结果表明,该算法可以有效去除雾度,保持图像的自然性。此外,与最新技术相比,该算法平均可将计算复杂度平均降低56.14%。

著录项

  • 来源
    《Signal processing》 |2014年第10期|380-398|共19页
  • 作者单位

    Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

    Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China,State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;

    Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;

    Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China,State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Negative correction; Dehazing; Image enhancement; The dark channel prior; Maximum-filter;

    机译:负校正;除雾图像增强;暗通道先行;最大过滤器;

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