Deconvolution by the Richardson-Lucy algorithm is achieved by minimizing the convex loss function derived in the last article
(1) 
with
, the scalar quantity to minimize, function of ideal image 
, linear captured image intensity laid out in 
rows and 
columns, corrupted by Poisson noise and blurred by the 
, the known two-dimensional Point Spread Function that should be deconvolved out of 
- , the output image resulting from deconvolution, ideally without shot noise and blurring introduced by the
- ** two-dimensional convolution
element-wise product
, element-wise natural logarithm
In what follows indices 
and 
, from zero to -1 and -1 respectively, are dropped for readability. Articles about algorithms are by definition dry so continue at your own peril.
So, given captured raw image blurred by known function , how do we find the minimum value of yielding the deconvolved image 
that we are after?