#Review of topaz detail software
#Review of topaz detail code

In addition to Photoshop and Dfine, I’ve used DxO PhotoLab’s “Prime” NR, Noise Ninja, the original Topaz DeNoise, and a few others. There may be some spots of the image I’d want to mask off and introduce a little more grain into, which seems similar to the “restore original detail” slider in DeNoise AI to apply it to the whole photo.

Colors transition cleanly into each other, as your eye perceives them to. It’s much sharper than Nik Dfine, yet the bursts of light are almost completely smooth, noise free, and banding free.

But on top of that, look at the sharpness of the snow on the mountains, and the bridge. Look at that! All the noise in the sky has been removed, and there’s no banding either – transitions are soft and gradual like just like it would have been at base ISO. Yeah, my response on first glance was an expletive too. After spending a couple days pixel peeping with DeNoise AI, it does indeed feel magic – but in a very good way! DeNoise AI outperforms Nik Dfine and Photoshop’s own noise reduction algorithms by a huge margin. OK, so maybe that’s a little too serious for noise reduction software, but you get the idea. Because I’m afraid-long into the foreseeable future-we’re not going to “solve world hunger with magic” or “use magic to write the perfect screenplay” any more than we are with AI. Whenever you see a story about an algorithm, replace buzzwords like “machine learning,” “artificial intelligence” and “neural network” with the word “magic.” Does everything still make grammatical sense? Is any of the meaning lost? If not, I’d be worried that something smells like bull. So how can you tell the bad from the good? There’s a quick trick I use to weed out suspicious examples. The helpfulness of algorithms varies drastically. 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: It at least passes the sniff test.Ī lot of eye rolling comes when terms like Artificial Intelligence or Machine Learning are used. 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. 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. 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.

#Review of topaz detail software

They trained the software with several million images containing noise, along with several million clean images. You’ll want to click on the images to zoom in so you can see the dramatic difference this tool makes.ĭeNoise AI claims to use machine learning as a means of identifying noise. I’ll show you some comparisons to other NR techniques I’ve used in this post. 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. Noise reduction has been the bane of my existence ever since I started astrophotography.

After seeing the results turn out so good, I needed to take some time to talk about this tool. 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.

#Review of topaz detail code

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):