对比度拉伸和直方图均衡之间的区别 [英] Difference between contrast stretching and histogram equalization

问题描述

我想知道对比度拉伸和直方图均衡化之间的区别.

I would like to know the difference between contrast stretching and histogram equalization.

我已经尝试过使用OpenCV并观察到结果，但是我仍然不了解这两种技术之间的主要区别.洞察力将是急需的帮助.

I have tried both using OpenCV and observed the results, but I still have not understood the main differences between the two techniques. Insights would be of much needed help.

推荐答案

首先定义对比度，

对比度是对图像范围"的度量；即其强度的分布程度.它有许多正式的定义，其中一个著名的是迈克尔逊:

Contrast is a measure of the "range" of an image; i.e. how spread its intensities are. It has many formal definitions one famous is Michelson’s:

他说contrast = ( Imax - Imin )/( Imax + I min )

对比度与图像的整体视觉质量紧密相关. 理想情况下，我们希望图像使用所有可用值范围 给他们.

Contrast is strongly tied to an image’s overall visual quality. Ideally, we’d like images to use the entire range of values available to them.

对比度拉伸和直方图均衡具有相同的目标:使图像使用所有可用值范围.

Contrast Stretching and Histogram Equalisation have the same goal: making the images to use entire range of values available to them.

但是他们使用不同的技术. 对比度拉伸的作用类似于映射

But they use different techniques. Contrast Stretching works like mapping

它将图像的最小强度映射到范围内的最小值(在上面的示例中84 ==> 0)

it maps minimum intensity in the image to the minimum value in the range( 84 ==> 0 in the example above )

以相同的方式，它将图像中的最大强度映射到范围内的最大值(在上面的示例中为153 ==> 255)

With the same way, it maps maximum intensity in the image to the maximum value in the range( 153 ==> 255 in the example above )

这就是为什么对比度拉伸"不可靠的原因，如果仅存在两个像素的0和255强度，则完全没有用.

This is why Contrast Stretching is un-reliable, if there exist only two pixels have 0 and 255 intensity, it is totally useless.

但是，更好的方法是使用概率分布的直方图均衡.您可以在此处

However a better approach is Histogram Equalisation which uses probability distribution. You can learn the steps here

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