Astin said:
Typically I set the amount to 80%-150%...
For raduis typically I set to 0.5-1.0...
For threashold I set to 1.
ah..this setting is closer to what i originally guessed that would work for a print of size between A5 and A4 :light: . [my test print verified that 190/1.7/3 is indeed somewhere crossing the "upper limit" already ie too sharp ;-) ]
nutek said:
A *generic* USM setting for web photos (640x480 or less) is:
85/1/4
mmm..i will take this as an arbitrary "starting point" to work on, and will use your "family love @zoo" as an arbitrary on-screen visual reference point :thumbsup: ;-)
r52lanc said:
For printing use 300/0.5/1 These settings assume that sharpening is set to 'normal' in the Canon 300D or 10D.
If you are resizing at all, remember that sharpening must come after resizing.
the 300/0.5/1 for the case of without in-camera sharpening...sounds logical..looks like the main difference is the first variable (ie the %) :think:
"sharpen after resize"...that means the effects of sharpening is non-linear w.r.t. to the change in image size. this sort of ties in with my suspision: while brushing my teeth, i roughly did soem mental maths...i dunno exactly how the sharpening algorithm works i terms of the actual maths involved; but i hazarded a quick guess--suppose the effect of sharpening, denoted by S (higher value of S =>sharper), is dependent on 3 basic veriables: percentage (%), radius (r) and threshold(t). ie we can define S = f(%,r,t). now the main experiment is to find out how S varies with image size I. ie there exists a relationship between S and I such that S=F(I). or in other words, we can say f(%,r,t)=F(I). rearranging, we get:
I = g(%, r, t). where g is a nolinear function. in more detailed terms,
L1.L2 = h. (%)*c1.(r)*c2.[k*(-c3.t)]
where L1,L2, cz, z=1,2,3; %, r, t are all positive with t being the only variable that can be zero (introduction of base k is to remove the singularity of the function g in the case where t=0)
now...the aim is to unravel the values of c1 ,c2 and c3, and most importantly, what the mystery parameter h is. it can either be a positive real number or a function. once we know all these 4 values, then given any image size of L1 by L2, we can input into the function g and derive a solution set of desired values for %, r, and t. that is , assumign that the assumption of the existence of function g is valid i nthe first place, which i believe to be so