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The new "Super Resolution" enhancement represents the next big leap in upsampling quality and it comes free with your Creative Cloud subscription. The results of Adobe's research and development are nothing short of amazing. These types of images typically do very poorly when enlarged resulting in heavy artifacts so they were excellent choices for training the algorithm. The programmers at Adobe concentrated on training the system using "challenging" images with lots of textures and small details. It’s much like how, as humans, seeing how a word is used in a sentence helps us to understand the meaning of that word."
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In other words, to understand how to up sample a given pixel, the computer needs some context, which it gets by analyzing the surrounding pixels. In the case of Super Resolution, the basic structure is called a “deep convolutional neural network,” a fancy way of saying that what happens to a pixel depends on the pixels immediately around it.
Teaching a computer to perform a task may sound complicated, but in some ways it’s similar to teaching a child - provide some structure and enough examples, and before long they’re doing it on their own. With enough examples covering all kinds of subject matter, the model eventually learns to up sample real photos in a naturally detailed manner. Specifically, we used millions of pairs of low-resolution and high-resolution image patches so that the computer can figure out how to upsize low-resolution images. "The idea is to train a computer using a large set of example photos.
The results are smoother with far fewer artifacts and interference patterns. The short version is that they have used machine learning to develop an entirely new algorithm for image enlargement and it has pushed Photoshop right up to the front of the pack when it comes to the quality of the enlarged images.Īs you can see from the example below, it does a noticeably better job than the existing Bicubic/Bilinear/Nearest Neighbor routines when it comes to enlargement. You can read all the details in this blog post from the ACR team explaining what it is and how they developed it.
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There hasn't been much in the way of advancement until last week when Adobe released an update for Photoshop and Camera Raw that included a new feature they are calling Super Resolution. The upsampling tools available to us reached a plateau over the last few years. Both do a fairly decent job of making enlargements with minimal quality loss.
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Genuine Fractals was later purchased by OnOne software and this excellent upsampling algorithm still lives on today as part of On1 Resize and On1 Raw.Īlien Skin (now rebranded as Exposure Software) has their own upsampling plugin called Blow Up and Topaz has one called Gigapixel AI. The results it produced were far superior to the native ones in Photoshop like Bicubic and Bilinear and it really was a game-changer at the time it was released to the public. This program used a branch of mathematics called fractal geometry to create an entirely new algorithm for enlargement that was capable of scaling images up or down with minimal loss of quality.
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The first great leap forward in enlarging software came with the release of a plugin called Genuine Fractals Print Pro from Alatamira Software all the way back in 1999. We need a better way to make bigger images! But they all have their limitations and the larger you try to go with an image, the more you will begin to see flaws like jagged edges and blurred details. "Preserve Details" works best for enlargements. "Nearest Neighbor" works well for hard edges.
They have names like "Bicubic", "Bilinear", "Nearest Neighbor", and "Preserve Details" and each one is designed to work with different types of images and for different enlargement (or reduction) tasks. Photoshop alone offers quite a number of different enlarging algorithms you can use when resizing an image. Software makers have created a variety of solutions over the years in an effort to solve the inherent problems of upsampling.
In order to make digital photos larger, images must be "upsampled" using a software algorithm to add more pixels.īecause most digital photos are made up of a grid or "bitmap" of pixels, enlarging these images frequently results in either blurry or jagged results as the software is forced to either increase the size of the pixel (which produces jagged results) or "guess" what additional pixels should look like (which produces blurry results). One question I get asked frequenlty by photographers of all varieties is: "How can I increase image resolution without losing too much image quality?"Įnlargements are something we deal with every day in the world of digital imaging and output.
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