//load ImageJ
%classpath config resolver scijava.public https://maven.scijava.org/content/groups/public
%classpath add mvn net.imagej imagej 2.0.0-rc-67

//create ImageJ object
ij = new net.imagej.ImageJ()

ij.op().help('addNoise')

input = ij.scifio().datasetIO().open("https://imagej.net/images/Tree_Rings.jpg")
ij.notebook().display(input)                                     

import net.imagej.ops.map.MapIterableToIterable
import net.imagej.ops.Ops
import net.imagej.ops.special.computer.Computers

//temporary type to pass through to the Op instance so that the Op knows what Type it is working on
tempType = input.firstElement()

//the minimum and maximum values allowed in the output - all values in the output 
//will be clamped to this value if below it in the input, so be careful
rangeMin = input.firstElement().getMinValue()
rangeMax = input.firstElement().getMaxValue()

//the standard deviation for the gaussian kernel
stdDev = 30d

//create the op that is passed through to the Mapper, using the help() op syntax above
noiseOp = Computers.unary(ij.op(), Ops.Filter.AddNoise.class, tempType, tempType, rangeMin, rangeMax, stdDev)

//output image
actualOutput = ij.op().create().img(input)

//run a map op over the image using the noise Op above
ij.op().run(MapIterableToIterable.class, actualOutput, input, noiseOp)

ij.notebook().display(actualOutput)