division and operates as a matrix but the division and the product (implicit after ) are element-wise. Also, can be calculated because we assume - The initial guess is known (and is typically set to be the experimental data) - The measurement function is known On the other hand is the experimental data. Therefore, equation () applied successively, provides an algorithm to estimate our ground truth by ascending (since it moves in the direction of the gradient of the likelihood) in the likelihood landscape. It has not been demonstrated in this derivation that it converges and no dependence on the initial choice is shown. Note that equation () provides a way of following the direction that increases the likelihood but the choice of the log-derivative is arbitrary. On the other hand equation () introduces a way of weighting the movement from the previous step in the iteration. Note that if this term was not present in () then the algorithm would output a movement in the estimation even if . It's worth noting that the only strategy used here is to maximize the likelihood at all cost, so artifacts on the image can be introduced. It is worth noting that no prior knowledge on the shape of the ground truth is used in this derivation. Software RawTherapee (since v.2.3) See also Deconvolution Wiener filter (deconvolution in the presence of additive noise) References Image processing Estimation theory