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Low Noise Color Space Transformation


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Color space transformation (or color correction) needs to be performed in typical imaging devices because the spectral sensitivity functions of the sensors deviate from the desired target color space. Several researchers have shown that when the color channels are correlated, color correction can result in annoying sensor noise amplification. We developed a color correction method that significantly alleviates this problem of noise amplification. The key idea is to use spatially varying color correction that adapts to local image statistics.

Summary of the method (Patent Pending)

There is a trade-off between color fidelity and the amplification of noise. By loosening the constraint of having a fixed color correction matrix for the entire image, a better trade-off can be obtained. The summary of the baseline procedure is given below.

  • Divide the image into non-overlapping blocks (e.g. 8 by 8).
  • For each block, compute the correlation matrix of the R, G and B channels and estimate the correlation matrix of the image sensor noise.
  • Compute the color correction matrix using the correlation matrices.
  • Apply the newly calculated color correction matrix to all the pixels in the block.
  • Proceed onto the next block and repeat previous steps for that block.

For more technical details, please refer to the following paper:

Suk Hwan Lim and Amnon Silverstein, “Spatially Varying Color Correction Matrices for Reduced Noise, ”accepted to IS&T Color Imaging Conference 2004, Scottsdale, AZ, November 2004. (pdf download)

Color correction examples

Check out some color corrected images with the new method compared with those corrected with conventional method.


For more information, please contact Suk Hwan Lim (sukhwan@hpl.hp.com) at Imaging Technology Dept., HP Labs.

  » Printing and Imaging Research Center
  » Imaging Systems Laboratory
Low Noise Color Correction
  » Example Images




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