where there’s smoke there’s fire

Topaz Labs DeNoise AI review by Jonathan Zdziarski (500px) – see the original blog post for more sample photos (DeNoise AI coupon code is available here):

Topaz Labs recently released DeNoise AI, a noise reduction tool for photographers, and so naturally I picked up a copy after reading about a sale on Nikon Rumors. After seeing the results turn out so good, I needed to take some time to talk about this tool. Noise reduction has been the bane of my existence ever since I started astrophotography. My wife and I have been chasing the Northern Lights for several years now, and trying to clean up our shots has always been a challenge. I’ll show you some comparisons to other NR techniques I’ve used in this post. You’ll want to click on the images to zoom in so you can see the dramatic difference this tool makes.

DeNoise AI claims to use machine learning as a means of identifying noise. They trained the software with several million images containing noise, along with several million clean images. Depending on which ML model they used, DeNoise AI likely developed a pattern recognition model based on neural nets or probabilistic models based on Bayes or Markov. It appears to use OpenCV’s ML library. In the end, DeNoise is probably a sophisticated pattern recognizer that associates noisy inputs with the most “learned” clean output, then maps the color back in. I’ve got somewhat of an ML background, and other than the use of “AI”, the product literature seems to describe a pretty straightforward, but novel approach to ML. It at least passes the sniff test.

A lot of eye rolling comes when terms like Artificial Intelligence or Machine Learning are used. To prevent abuse of these phrases, I recommend following Hannah Fry’s litmus test, from her book, Hello World: Being Human in the Age of Algorithms:

Read More »