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Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference

机译:利用双目刚注意到的差异增强立体图像的清晰度

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摘要

In this paper, we propose a new sharpness enhancement algorithm for stereo images. Although the stereo image and its applications are becoming increasingly prevalent, there has been very limited research on specialized image enhancement solutions for stereo images. Recently, a binocular just-noticeable-difference (BJND) model that describes the sensitivity of the human visual system to luminance changes in stereo images has been presented. We introduce a novel application of the BJND model for the sharpness enhancement of stereo images. To this end, an overenhancement problem in the sharpness enhancement of stereo images is newly addressed, and an efficient solution for reducing the overenhancement is proposed. The solution is found within an optimization framework with additional constraint terms to suppress the unnecessary increase in luminance values. In addition, the reliability of the BJND model is taken into account by estimating the accuracy of stereo matching. Experimental results demonstrate that the proposed algorithm can provide sharpness-enhanced stereo images without producing excessive distortion.
机译:在本文中,我们提出了一种新的立体图像清晰度增强算法。尽管立体图像及其应用正变得越来越普遍,但是对于用于立体图像的专用图像增强解决方案的研究非常有限。最近,已经提出了一种双眼正视差(BJND)模型,该模型描述了人类视觉系统对立体图像中亮度变化的敏感性。我们介绍了BJND模型在立体图像锐度增强方面的新颖应用。为此,新解决了立体图像的锐度增强中的过度增强问题,并且提出了用于减少过度增强的有效解决方案。该解决方案可在带有其他约束条件的优化框架中找到,以抑制亮度值的不必要增加。另外,通过估计立体声匹配的准确性考虑了BJND模型的可靠性。实验结果表明,该算法可以提供清晰的立体图像,而不会产生过多的失真。

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