Consider installing the GeoStatsImages.jl package.

Unconditional

An example of unconditional simulation (i.e. simulation without data). This is the original Efros-Freeman algorithm for texture synthesis with a few additional options.

using ImageQuilting
using GeoStatsImages

TI = training_image("Strebelle")
reals = iqsim(TI, 62, 62, 1, size(TI)..., nreal=3)

TI = training_image("StoneWall")
reals, cuts, voxs = iqsim(TI, 13, 13, 1, size(TI)..., nreal=3, debug=true)

Hard data

Hard data (i.e. point data) can be honored during simulation. This can be useful when images represent a spatial property of a physical domain. For example, the subsurface of the Earth is only known with certainty at a few well locations.

using ImageQuilting
using GeoStatsImages

TI = training_image("Strebelle")

data = HardData()
push!(data, (50,50,1)=>1)
push!(data, (190,50,1)=>0)
push!(data, (150,170,1)=>1)
push!(data, (150,190,1)=>1)

reals, cuts, voxs = iqsim(TI, 30, 30, 1, size(TI)..., hard=data, debug=true)

Soft data

Sometimes it is also useful to incorporate auxiliary variables defined in the domain, which can guide the selection of patterns in the training image. This example shows how to achieve this texture transfer efficiently.

using ImageQuilting
using GeoStatsImages
using Images: imfilter_gaussian

TI = training_image("WalkerLake")
truth = training_image("WalkerLakeTruth")

G(m) = imfilter_gaussian(m, [10,10,0])

data = SoftData(G(truth), G)

reals = iqsim(TI, 27, 27, 1, size(truth)..., soft=data, nreal=3)