# 【计算机视觉】FFC平场校正算法剖析

（1）光照不均匀

（2）镜片中心和镜片边缘的响应不一致

（3）成像器件各像元响应不一致（光敏元自身的非均匀性工艺）

（4）固定的图像背景噪声等等．

1）Fixed Pattern Noise (FPN): 暗场，固定图像噪声校正

2）Photo Response Non Uniformity (PRNU)：明场，图像非均匀性响应校正

3）Lens and light source non-uniformity：明场，镜头与光源非均匀性校正

• FPN校正时，像素值必须在1DN与127DN（可理解为灰度值）之间；PRNU校正时，像素值必须在128DN与254DN之间。校正前最好首先使用”gl”命令确认DN值是否满足要求。

①相机线性响应过程中使用

②暗场校正（FPN）

a)首先要获取系统的暗场像素值（取平均值）；获得该值的目的在于得到CCD在特定的环境下（光亮、温度、时间相同）产生的暗像素值的大小，用一下公式表示

Goff是图像的偏置量,一般为一常量,i(x,y)为探测器在相应工作条件下的暗电流,以像元考虑的话,单位可以用电子数/秒 像元来表示;t为获取一幅图像的时间;K为转换关系,相应的单位可以用图像灰度/电子数表示

b)通过对均匀光场的成像以获得一幅用于平场校正的参考图像GR(x,y),该图像原则上要求用均匀光场X0进行照明且时间与暗本底图像GB(x,y)相同,并且照明光场光照水平尽量接近饱和照明条件,参考图像GR(x,y)可表示为

C)被用于校正的图像G(x,y)表示为

③面阵CCD非均匀性校正计算方式

?(?)表示在辐照度为?时面阵CCD所有像元的平均灰度值，?(?)??表示未经校正的图像第i行第j列像元的实际输出灰度值，???为对应像素位置的像元增益，???为对应像素位置的像元偏移量，M,N分别为图像行列。两点校正方法需要选取两个定标点，由此可推出CCD各像元的增益和偏移量，

4.均匀性评估

### Proper correction

Use this technique on brightfield images. You can correct uneven illumination or dirt/dust on lenses by acquiring a “flat-field” reference image with the same intensity illumination as the experiment. The flat field image should be as close as possible to a field of view of the cover slip without any cells/debris. This is often not possible with the experimental cover slip, so a fresh cover slip may be used with approximately the same amount of buffer as the experiment.

1. Open both the experimental image and the flat-field image.

2. Click the Select all button on the flat-field image and measure the average intensity. This value, the k1 value, will appear in the results window.

3. Use the Image Calculator plus plugin (Analyze › Tools › Calculator plus).

4. i1 = experimental image; i2 = flat-field image; k1 = mean flat-field intensity; k2 = 0. Select the ” Divide” operation.

This can also be done using the Process › Image Calculatorfunction with the 32-bit Result option checked. Then adjust the brightness and contrast and convert the image to 8-bit.

### Pseudo-correction

Sometimes it is not possible to obtain a flat-field reference image. It is still possible to correct for illumination intensity, though not small defects like dust, by making a “pseudo-flat field” image by performing a large-kernel filter on the image to be corrected. For those working with DIC images, this is particularly useful because they generally have an intrinsic, and distracting, gradient in illumination.

This can be accomplished simply by subtracting the Gaussian-blurred image version of the image.

This can also be used with stacks for brightfield time-courses that vary in intensity with time. Doing this with stacks can be time consuming.

The first RAW image (top) is pseudo-flat field corrected. Here the pseudo-flat field corrects for the uneven illumination, but does not correct for the dust specks. Look at this compared to the result of a proper flat-field correction above.

BigStitcher ›Flatfield Correction

Flat-field Correction方法原理分析

Original: https://www.cnblogs.com/carsonzhu/p/16347432.html
Author: 小金乌会发光－Z&M
Title: 【计算机视觉】FFC平场校正算法剖析

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