The Huawei P40 Pro looks like the winner in the smartphone zoom race, achieving DxOMark Zoom Score of 115, the all-time high. However, its outstanding camera hardware, utilizing a 125mm f/3.4 telephoto module, is not the only key to success. Achieving the best results would not be possible without using a computational super resolution zoom technology. We did some testing to check how good it is and if better results could be achieved if using the leading Super Resolution technology from Almalence.
We will start with a side-by-side comparison and then discuss some interesting features of Huawei’s SR which we found during the testing. For the testing, we captured:
- several JPEG images with the built-in camera app at 10x, those came out pretty different so we used the best one for comparison;
- a series of RAW images with the 5x telephoto camera module, which were then processed with 2x Almalence Super Resolution.
The pictures were captured indoor, in good office lighting (~700 Lux). Note, as we used RAW images for processing, the colors in Almalence SR output are somewhat off.
Comparing the ability to resolve fine details shows a dramatic improvement when using Almalence Super Resolution Zoom:
A strange effect in the next example, most of the fine text is “washed out” in the P40 Pro image. That can be caused by extreme noise filtering or input frames misalignment/deghosting.
Also note the highlighted character. It looks like Huawei’s algorithm employs a kind of a neural network, which tried to “guess” the object but in this case made a wrong guess. (We will show more examples of that NN’s job below)
A testing with a wedge chart, Almalence SR Zoom increases the effective resolution by ~20..25% more than Huawei’s built-in algorithm:
Getting back to the P40 Pro’s [supposedly] neural network, an interesting example below. First of all, the NN did an absolutely fantastic job resolving the hair (look at the areas 1 and 2). This looks like something beyond the normal capabilities of super resolution algorithms, which makes us convinced a neural network was involved. Exploring the image further, however, we can see that in some areas (e.g. area 3) the picture looks very detailed but actually unnatural (and yes, different from the original), so the NN made a visually nice, but actually a wrong guess. In the area 4, the algorithm “resolved” the eye in a way that it distorted the eyelid and iris geometry, making the two eyes looking at different directions; it also guessed the bottom eyelashes in a way that they look like growing from the eyeball, not the eyelid, which looks rather unnatural.
Huawei’s result looks more detailed, however in some areas those details are unnatural and do not reflect the original object.
Summary: while the Huawei P40 Pro is clearly the winner in telephoto camera module hardware design, its computational zoom algorithm is not yet doing the best possible job. While having some advantages over Almalence’s Super Resolution Zoom in resolving certain kinds of objects, it could be better in terms of overall resolution capability. It would be really interesting to see what those algorithms could do if combined together, likely that would make an all-time best digital zoom technology.