I am currently working with an existing implementation of Perlin noise, which came bundled with a bunch of code I am trying to clean up. The code in question is severely under-tested, and I would like to make sure that each of its components receives proper testing in case there are any hidden bugs.
However, I am not sure how I would go about testing the correctness of the Perlin noise implementation in this case. I welcome all suggestions.
This is a tough problem & probably doesn't have a single best solution.
For some images properties, you might be able to perform automated tests using Computer Vision techniques. I.E. if your Perlin noise output is supposed to be tile-able, an edge detection filter might be able to catch problems. I've also had some good results using FFT filters when I was working on an image classifier for perlin noise based wood grain textures. In my experience, implementing such tests can easily take more time then building the code being tested. To minimize that, I'd stick with libraries like OpenCV, Octave, etc. Also, this approach depends on having known good output in order to build your tests.
From a certain perspective, Perlin noise is a type of random number generator. To that end, you might be able to use RNG test suites like the NIST Statistical Test Suite or the Diehard tests. This approach depends on having known good output in order to build your tests.
Finally, you could build tests that output the results to file & then perform a manual confirmation of each against expected results. For convenience, you could load collections of images via a web page & maybe even integrate reporting check boxes to collect pass/fail responses from your tester. This solution is the best I've come up with for testing properties that are difficult, impossible or impracticable to quantify. I.E. I only know my particle effect is correct when I see it.