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See Better and Further with Super Res Zoom on the Pixel 3

Monday, October 15, 2018

Posted by Bartlomiej Wronski, Software Engineer and Peyman Milanfar, Lead Scientist, Computational Imaging(Updated August 6, 2020: The work described in this blogpost was presented at SIGGRAPH 2019, and has been published in the ACM Transactions on Graphics.)DSLR camerassinglemany framesSuper Res Zoom means that if you pinch-zoom before pressing the shutter, you’ll get a lot more details in your picture than if you crop afterwards.

Crops of 2x Zoom: Pixel 2, 2017 vs. Super Res Zoom on the Pixel 3, 2018.

The Challenges of Digital Zoomlinear interpolation methodsRAISRColor Filter Arrays and Demosaicingdemosaicingcolor filter arrayBayer

A Bayer mosaic color filter. Every 2x2 group of pixels captures light filtered by a specific color — two green pixels (because our eyes are more sensitive to green), one red, and one blue. This pattern is repeated across the whole image.

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Demosaicing reconstructs missing color information by using neighboring neighboring pixels.

From Burst Photography to Multi-frame Super-resolutionburst photographyHDR+ algorithmknown for more than a decadedrizzlemultiplesuper-resolution

As compared to the standard demosaicing pipeline that needs to interpolate the missing colors (top), ideally, one could fill some holes from multiple images, each shifted by one pixel horizontally or vertically.

Image AliasingAliasingMoiré patternsunfortunate choice of wardrobe

Left: High-resolution, single image of a table edge against a high frequency patterned background, Right: Different frames from a burst. Aliasing and Moiré effects are visible between different frames — pixels seem to jump around and produce different colored patterns.

Practical Super-resolution Using Hand MotionOptical Image Stabilization

Effect of hand tremor as seen in a cropped burst, after global alignment.

Overcoming the Challenges of Super-resolution

A single image from a burst is noisy, even in good lighting. A practical super-resolution algorithm needs to be aware of this noise and work correctly despite it. We don’t want to get just a higher resolution noisy image - our goal is to both increase the resolution but also produce a much less noisy result.

Left: Single frame frame from a burst taken in good light conditions can still contain a substantial amount of noise due to underexposure. Right: Result of merging multiple frames after burst processing.

Motion between images in a burst is not limited to just the movement of the camera. There can be complex motions in the scene such as wind-blown leaves, ripples moving across the surface of water, cars, people moving or changing their facial expressions, or the flicker of a flame — even some movements that cannot be assigned a single, unique motion estimate because they are transparent or multi-layered, such as smoke or glass. Completely reliable and localized alignment is generally not possible, and therefore a good super-resolution algorithm needs to work even if motion estimation is imperfect.

Because much of motion is random, even if there is good alignment, the data may be dense in some areas of the image and sparse in others. The crux of super-resolution is a complex interpolation problem, so the irregular spread of data makes it challenging to produce a higher-resolution image in all parts of the grid.

To effectively merge frames in a burst, and to produce a red, green, and blue value for every pixel without the need for demosaicing, we developed a method of integrating information across the frames that takes into account the edges of the image, and adapts accordingly. Specifically, we analyze the input frames and adjust how we combine them together, trading off increase in detail and resolution vs. noise suppression and smoothing. We accomplish this by merging pixels along the direction of apparent edges, rather than across them. The net effect is that our multi-frame method provides the best practical balance between noise reduction and enhancement of details.

Left: Merged image with sub-optimal tradeoff of noise reduction and enhanced resolution. Right: The same merged image with a better tradeoff.

To make the algorithm handle scenes with complex local motion (people, cars, water or tree leaves moving) reliably, we developed a robustness model that detects and mitigates alignment errors. We select one frame as a “reference image”, and merge information from other frames into it only if we’re sure that we have found the correct corresponding feature. In this way, we can avoid artifacts like “ghosting” or motion blur, and wrongly merged parts of the image.

A fast moving bus in a burst of images. Left: Merge without robustness model. Right: Merge with robustness model.

Pushing the State of the Art in Mobile PhotographyPortrait modeHDR+album

Left: Crop of 7x zoomed image on Pixel 2. Right: Same crop from Super Res Zoom on Pixel 3.

An illustrative animation of Super Res Zoom. When the user takes a zoomed photo, the Pixel 3 takes advantage of the user’s natural hand motion and captures a burst of images at subtly different positions. These are then merged together to add detail to the final image.AcknowledgementsSuper Res Zoom is the result of a collaboration across several teams at Google. The project would not have been possible without the joint efforts of teams managed by Peyman Milanfar, Marc Levoy, and Bill Freeman. The authors would like to thank Marc Levoy and Isaac Reynolds in particular for their assistance in the writing of this blog.

The authors wish to especially acknowledge the following key contributors to the Super Res Zoom project: Ignacio Garcia-Dorado, Haomiao Jiang, Manfred Ernst, Michael Krainin, Daniel Vlasic, Jiawen Chen, Pascal Getreuer, and Chia-Kai Liang. The project also benefited greatly from contributions and feedback by Ce Liu, Damien Kelly, and Dillon Sharlet. How to get the most out of Super Res Zoom?

Pinch and zoom, or use the + button to increase zoom by discrete steps.

Double-tap the preview to quickly toggle between zoomed in and zoomed out.

Super Res works well at all zoom factors, though for performance reasons, it activates only above 1.2x. That’s about half way between no zoom and the first “click” in the zoom UI.

There are fundamental limits to the optical resolution of a wide-angle camera. So to get the most out of (any) zoom, keep the magnification factor modest.

Avoid fast moving objects. Super Res Zoom will capture them correctly, but you will not likely get increased resolution.

* It’s worth noting that the situation is similar in some ways to how we see — in human (and other mammalian) eyes, different eye cone cells are sensitive to some specific colors, with the brain filling in the details to reconstruct the full image.^↩