according to Eqn. (9) in the paper, variance over f(x) is x*/k+sigma/(kk) = u+sigma/(kk) .
it seems this variance dependents on k and sigma or sigma/(kk).
obviously k and sigma both are dependent on ISO.
for sigma/(kk), because k = ga, sigma = ggsigmaD+sigmaR,
so sigma/(kk) = sigmaD/(aa) + sigmaR/(aagg), which is also dependent on g, aka. ISO.
i also tried to calculate sigma/(k*k) over different iso value, using "class KSigma" in the source code with estimated parameters provided in the code: K_coeff=[0.0005995267, 0.00868861],
B_coeff=[7.11772e-7, 6.514934e-4, 0.11492713],
and find it is not Constant.
so How to understand k-Sigma transform can make signal ISO-Independent?
according to Eqn. (9) in the paper, variance over f(x) is x*/k+sigma/(kk) = u+sigma/(kk) .
it seems this variance dependents on k and sigma or sigma/(kk).
obviously k and sigma both are dependent on ISO.
for sigma/(kk), because k = ga, sigma = ggsigmaD+sigmaR,
so sigma/(kk) = sigmaD/(aa) + sigmaR/(aagg), which is also dependent on g, aka. ISO.
i also tried to calculate sigma/(k*k) over different iso value, using "class KSigma" in the source code with estimated parameters provided in the code: K_coeff=[0.0005995267, 0.00868861],
B_coeff=[7.11772e-7, 6.514934e-4, 0.11492713],
and find it is not Constant.
so How to understand k-Sigma transform can make signal ISO-Independent?