While these files are unprocessed, each program handled them slightly differently, the most important distinction being ACR automatically applying lens corrections. To compare, I grabbed some raw files from my Mavic Air 2 (to represent aerial shots) and my Nikon Z 7 (representing architecture and product photography, as well as higher resolution). While there are some differences between how these programs work, with PS’s feature integrated into ACR instead of being a standalone program and Gigapixel offering some more options for customizing the processing, the end results are perfectly comparable.įor these tests, I wanted to take a look at a couple of different types of images that I often shoot and that often benefit from more resolution. Depending on how this is implemented, it can be pretty computationally intense, relying on your computer’s GPU to perform a lot of work. This model can then up-sample the photos and “create” details to fill in the blanks.

Gigapixel is built around a similar principle: train a machine learning model with a set of low- and high-resolution image pairs so that the computer eventually learns what a low-resolution area could look like in high resolution. It’s even been implemented into existing consumer products, most notably in Topaz’s Gigapixel AI software. AI upscaling has been around for a while.

Photoshop Super Resolution Versus Topaz Gigapixel AI Currently available in Camera Raw 13.2 and coming soon to Lightroom and Lightroom Classic, Super Resolution uses a machine learning model to “intelligently enlarge photos while maintaining clean edges and preserving important details.” In practice, it’s a one-click way to quadruple your photo’s megapixel count, while retaining much more detail than “dumb” upscaling, like bicubic and nearest neighbor methods. If you’re not caught up on Photoshop’s notes, Adobe Camera Raw has received a new feature called Super Resolution.

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