Solving the Inversion Problem with RGB Filters

In summary, the individual is seeking help in establishing an algorithm for measuring unknown spectrum based on known spectrum data. They have attached an image with the equation and mention that RGB filters apply to the true spectrum they are looking for. They also mention that they can figure out RGB with only one known spectrum, but want to bring in more data for accuracy. They are looking for assistance in creating a training set and all values are vector containing 300 numbers.
  • #1
Edward Zhang
1
0
I have some measurement data for known spectrum. Now I want to establish a algorithm for later measurement of unknown spectrum. I attached the equation in the image. r g b filters apply to the true spectrum I I am looking for. R G B are the measurement getting from the device. I1, I2, I3 are different spectrum measured. I want to based on the measured RGB values and known spectrum I1 I2 I3 to figure out r g b, so later I can use it for retrieve for other unknown I. I can figure out r g b with only one I, but I would thinking that's not accurate, so I want to bring in more known spectrum to form a training set. Anyone can help me with that? All the values are vectors contain 300 numbers.
[PLAIN][PLAIN][PLAIN]https://www.physicsforums.com/file:///C:\Users\zcj\AppData\Roaming\Tencent\Users\403532326\QQ\WinTemp\RichOle\_M4KG7`AMP48RLCUO%GG$WF.jpg
[PLAIN][PLAIN][PLAIN]https://www.physicsforums.com/file:///C:\Users\zcj\AppData\Roaming\Tencent\Users\403532326\QQ\WinTemp\RichOle\_M4KG7`AMP48RLCUO%GG$WF.jpg
[PLAIN][PLAIN][PLAIN]https://www.physicsforums.com/file:///C:\Users\zcj\AppData\Roaming\Tencent\Users\403532326\QQ\WinTemp\RichOle\_M4KG7`AMP48RLCUO%GG$WF.jpg
[PLAIN][PLAIN][PLAIN]https://www.physicsforums.com/file:///C:\Users\zcj\AppData\Roaming\Tencent\Users\403532326\QQ\WinTemp\RichOle\_M4KG7`AMP48RLCUO%GG$WF.jpg
[PLAIN][PLAIN][PLAIN]https://www.physicsforums.com/file:///C:\Users\zcj\AppData\Roaming\Tencent\Users\403532326\QQ\WinTemp\RichOle\_M4KG7`AMP48RLCUO%GG$WF.jpg
[PLAIN][PLAIN][PLAIN]https://www.physicsforums.com/file:///C:\Users\zcj\AppData\Roaming\Tencent\Users\403532326\QQ\WinTemp\RichOle\_M4KG7`AMP48RLCUO%GG$WF.jpg
 

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  • #2
Thanks for the post! Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
 

1. What is the "inversion problem" in relation to RGB filters?

The "inversion problem" refers to the difficulty in accurately estimating the true colors of an object when using RGB filters to capture images. This is because RGB filters only capture a limited range of colors, and the resulting images can appear distorted or inaccurate compared to the true colors of the object.

2. How can RGB filters be used to solve the inversion problem?

RGB filters can be used to solve the inversion problem by capturing multiple images with different filter combinations, and then using computational algorithms to combine the images and estimate the true colors of the object. By using a larger number of filter combinations, the accuracy of the estimation can be improved.

3. What are the limitations of using RGB filters to solve the inversion problem?

The main limitations of using RGB filters to solve the inversion problem are that it can be time-consuming and computationally intensive to capture and combine multiple images. Additionally, the accuracy of the estimation is limited by the number and quality of the filters used.

4. Are there any other methods for solving the inversion problem besides using RGB filters?

Yes, there are other methods for solving the inversion problem, such as using multispectral imaging or hyperspectral imaging. These methods capture a wider range of colors and can provide more accurate estimations of the true colors of an object.

5. How important is it to solve the inversion problem in scientific research?

The inversion problem is crucial in scientific research, particularly in fields like remote sensing and astronomy where accurate color information is essential for understanding and analyzing images. Solving the inversion problem allows for more precise and reliable data to be collected and analyzed, leading to better scientific discoveries and advancements.

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