# How to balance gaussian distribution for image processing?

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Jaeden Kenworthy on 1 Nov 2022
Answered: Ashutosh Bajpai on 8 Dec 2022
Hello,
I am doing image processing where, in the experiment, we are analyzing the induced fluorescence of a tracer with a laser. And because of the way the tracer absorbed the laser, the particles farther away are illuminated less, leading to an imbalanced gaussian distribution. How could I account for this and balance the distribution?

Ashutosh Bajpai on 8 Dec 2022
There are several ways you could try to balance the distribution of the induced fluorescence in your image processing experiment. Some of the other approaches you could try to balance the distribution of fluorescence in your images include:
1. Histogram equalization: This is a technique for redistributing the pixel intensities in an image so that they are more evenly distributed across the range of possible values. This can help to improve the overall contrast and detail in the image, and can also reduce the impact of imbalanced fluorescence levels.
2. Gaussian blur: This is a technique for smoothing out the fluorescence levels in an image by applying a Gaussian filter to the pixel values. This can help to reduce the impact of outliers in the fluorescence distribution and make the overall distribution more balanced.
3. Adjusting camera exposure: By adjusting the exposure settings on the camera, you can control how much light is captured by the image sensor. This can affect the range of fluorescence intensities that are captured in the image, and can help to balance the distribution of fluorescence levels.
4. Logarithmic transformation: This is a mathematical operation that can be applied to the pixel values in an image to compress the dynamic range of the fluorescence levels. This can make the fluorescence distribution more evenly balanced and can help to improve the overall contrast and detail in the image.
5. Contrast stretching/limiting: These are techniques for expanding or limiting the range of pixel intensities in an image. By stretching or limiting the range of fluorescence intensities, you can make the distribution of fluorescence levels more balanced and improve the overall contrast and detail in the image.
6. Thresholding: This is a technique for identifying and removing outliers in the fluorescence distribution. By setting a threshold for the fluorescence levels, you can identify pixels that fall outside of a certain range and either remove them or replace them with values that are more consistent with the rest of the distribution.
7. Morphological operations: These are techniques for transforming the shape or structure of an image, often by applying a simple mathematical operation to each pixel in the image. By applying a morphological operation, such as dilation or erosion, to the fluorescence levels in an image, you can smooth out the distribution and make it more balanced.

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