基于全局极限的伽玛校正去雾图像增强

C. Hsieh
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引用次数: 0

摘要

单幅图像雾霾去除一直是图像增强或图像恢复界的一个活跃研究课题。大多数研究人员都致力于通过修改算法本身来提高除雾性能。在本文中,我们提出了一种后处理方案,即伽玛校正,以增强经过一些去雾算法后的去雾图像的视觉质量。众所周知,传统的伽玛校正(CGC)在许多情况下存在严重的畸变。这就阻碍了CGC在去雾图像增强中的应用。根据我们的观察,问题是由局部限制引起的,局部限制分别在RGB分量中发现。为了解决这个问题,引入了全局极限伽玛校正(GCGL),其中全局极限由RGB分量中的最小值和最大值获得。该算法得到了验证,并应用于去雾图像增强。仿真结果表明,在给定的例子中,去雾图像的视觉质量得到了普遍的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dehazed Image Enhancement by a Gamma Correction with Global Limits
Single image haze removal has been an active research topic in the image enhancement or image restoration community. Most of researchers have put their effort to improve the dehazing performance through modifying algorithm itself. In this paper, we propose a post-processing scheme, which is a gamma correction, to enhance the visual quality of a dehazed image after some dehazing algorithm. It is well-known that the conventional gamma correction (CGC) severely suffers from the due distortion in many cases. Thus, it hinders the application of the CGC to dehazed image enhancement. By our observations, the problem is caused by the local limits, which are found in the RGB components separately. To relieve the problem, a gamma correction with global limits (GCGL) is introduced, where the global limits are obtained by the minimum and maximum in the RGB components. The proposed GCGL is justified and applied to dehazed image enhancement. The simulation results indicate that the visual quality of dehazed images have been generally improved in the given examples.
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