$$\hat f(x, y) = g(x, y)-\frac{\sigma_n^2}{\sigma_L^2}\left[g(x, y)-m_L\right]$$

What are the meanings of the following terms:

  • $m_L$
  • ${\sigma_\eta}^2$
  • ${\sigma_L}^2$

Here we see that $m_L$ is subtracted from the Image and then the whole term is multiplied by $\frac{{\sigma_\eta}^2}{{\sigma_L}^2} $. Then, the whole term is again subtracted from $g(x,y)$.

What does that actually mean?

  • 2
    $\begingroup$ Have these equations come to you during your dream? What was the title of the book or paper? what about the chapter and section names? Before asking a question you should better provide a more accurate and precise description of the situation you are in. $\endgroup$
    – Fat32
    Mar 17, 2016 at 15:09

1 Answer 1


A wild guess. $L$ denotes a region $R$ around pixel $g(x,y)$, potentially a symmetric one of size $(2L+1)\times (2L+1)$. $m_L$ and $\sigma_L^2$ are the average and variance in $R$. And $\sigma_n^2$? Probably the estimated noise variance over the whole image, supposed stationary.

In a flat region with noise $\sigma_L^2 \sim \sigma_n^2$, the result could be close to the average. Around edges, one expect an higher response, so sharper. Potential problems arise when $\sigma_L \ll \sigma_n $. Another interpretation:

$$\hat{f}(x,y)=(1-\frac{\sigma_n^2}{\sigma_L^2})g(x,y)+ \frac{\sigma_n^2}{\sigma_L^2}m_L(x,y)$$ is a sort of weighted mean (since weights sum to one) of the image and its smoothed version, whose dependency on $(x,y)$ is made more explicit.

  • $\begingroup$ I think you forgot the $g(x, y)$ as multiplicative factor on the RHS parenthesis. $\endgroup$
    – Gilles
    Mar 17, 2016 at 22:28

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