如何计算图像有噪声和几何失真？ [英] How to calculate an image has noise and Geometric distortion or not?

问题描述

我需要在iphone中做一个应用程序，需要计算噪声，几何变形图像中的其他失真。如何做到这一点？我做了一些图像处理的东西用opencv + iphone。但我不知道如何计算这些参数。

I need to make an application in iphone which needs to calculate noise, geometric deformation other distortions in an image. How to do this? I have done some image processing stuff with opencv + iphone. But I dont know how to calculate these parameters.

1）如何计算图像中的噪声？

1) How to calculate noise in an image?

2）什么是几何变形和如何计算图像的几何变形？

2) What is geometric deformation and how to calculate geometric deformation of an image?

3）几何变形和失真在图像滤波器方面是相同的参数吗？

3) Is geometric deformation and distortion are same parameters in terms of image filter? or any other distortions available to calculate an image is good quality or not?

输入：我的图片是直播视频流中的脸部图片。

Input: My image is a face image in live video stream.

推荐答案

我建议您阅读一些关于图像处理的文献，例如 Gonzalez& Woods 。

I advise you to read some literature about image processing, for example Gonzalez & Woods.

1）单图像噪声计算的最简单的方法是计算图像和其平滑拷贝之间的标准偏差。对于平滑我建议你使用简单的中值滤波器3×3像素（或更多）的样本。中位数对数据爆发不敏感，所以像盐 - 胡椒这样的名词不会恶化统计数据。
在曝光过度或曝光不足的情况下，此类方法可能会给您带来不良的结果，在这种情况下，您可以计算图像的FFT，并使用高频分量进行噪声估计。

1) The simplest method of noise calculation by single image is to compute standard deviation between image and its smoothed copy. For smoothing I recommend you to use simple median filter by sample of 3x3 pixels (or more). Median is non-sensitive to outbursts of data, so noice like "salt-n-pepper" won't worsen statistics. In cases of overexposed or underexposed images such method can give you bad results, in that case you can calculate FFT of image and use a high frequency components for noise estimation.

2），3）只有在知道图像上应该的情况下，才可能计算几何变形。例如，如果您使用带有二次网格的mire（光学标准具），您可以在图片上找到线条（例如 Canny边缘检测器），并计算失真，像散和一些其他像差。如果您确定该图片有一些直线，也可以这样做。
散焦可以通过对图像边缘的分析或借助图像小波变换来计算。
还有更多不同的图像分析方法。例如，通过分析彩色图像，可以估计色差等。
但我重复一遍：在通常情况下，这种操作是不可能的。他们都有一些特殊的申请案件。

2), 3) Calculation of geometric deformation is possible only if you know, what should be on image. For example, if you use mire (optical etalon) with quadratic grid, you can find lines on your image (for example by Canny edge detector) and compute distortion, astigmatism and some other aberrations. This could be done also if you sure that image have some straight lines. Defocusing can be computed from analysis of edges on image or with help of image wavelet transform. There also much more different methods for image analysing. For example, by analysis of colour image you can estimate chromatic aberration and so on. But I repeat: in common case this operations are impossible. They all have some particular cases of application.

阅读图片质量< a>：没有标准的这个术语，在每一个特定的情况下，你可以使用一个或多个简单的特征来识别图像是否好。

Read about image quality: there are no standard for this term, in every particular case you can use one or more simple characteristics to recognize whether image good or not.

建议你使用不同种类的人工制品和质量制作大量照片，然后对其统计数据，小波组成和RGB分量相关性进行简单分析。 BTW，为了使彩色图像的分析对其亮度不太敏感，我建议您在 HSV 色彩空间中工作（但估计色度您需要使用RGB组件准确工作的像差）。

In you case I'd advice you to make a lot of photos with different kind of artefacts and quality, then make simple analysis of their statistics, wavelet compositions and R-G-B components correlation. BTW, to make analysis of colour image less sensitive to its brightness I recommend you to work in HSV colorspace (but to estimate chromatic aberration you need to work exactly with RGB components).

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