![]() ![]() ![]() Most real demosaicing algorithms are more complicated than this, but they still lead to imperfect results and artifacts - as we are limited to only partial information. In its simplest form, this could be achieved by averaging from neighboring values. * Demosaicing starts by making a best guess at the missing color information, typically by interpolating from the colors in nearby pixels, meaning that two-thirds of an RGB digital picture is actually a reconstruction!ĭemosaicing reconstructs missing color information by using neighboring neighboring pixels. This pattern is repeated across the whole image.Ī camera processing pipeline then has to reconstruct the real colors and all the details at all pixels, given this partial information. 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. These are arranged in a Bayer pattern as shown in the diagram below.Ī Bayer mosaic color filter. To capture real colors present in the scene, cameras use a color filter array placed in front of the sensor so that each pixel measures only a single color (red, green, or blue). In typical consumer cameras, the camera sensor elements are meant to measure only the intensity of the light, not directly its color. Reconstructing fine details is especially difficult because digital photographs are already incomplete - they’ve been reconstructed from partial color information through a process called demosaicing. While we still use RAISR to enhance the visual quality of images, most of the improved resolution provided by Super Res Zoom (at least for modest zoom factors like 2-3x) comes from our multi-frame approach. These magnify some specific image features such as straight edges and can even synthesize certain textures, but they cannot recover natural high-resolution details. In contrast, most modern single-image upscalers use machine learning (including our own earlier work, RAISR). Traditionally, this is done by linear interpolation methods, which attempt to recreate information that is not available in the original image, but introduce a blurry- or “plasticy” look that lacks texture and details. Super Res Zoom on the Pixel 3, 2018.ĭigital zoom is tough because a good algorithm is expected to start with a lower resolution image and "reconstruct" missing details reliably - with typical digital zoom a small crop of a single image is scaled up to produce a much larger image. Super 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.Ĭrops of 2x Zoom: Pivs. This results in greatly improved detail that is roughly competitive with the 2x optical zoom lenses on many other smartphones. The Super Res Zoom technology in Pixel 3 is different and better than any previous digital zoom technique based on upscaling a crop of a single image, because we merge many frames directly onto a higher resolution picture. With the new Super Res Zoom feature on the Pixel 3, we are challenging that notion. As compared to the optical zoom capabilities of DSLR cameras, the quality of digitally zoomed images has not been competitive, and conventional wisdom is that the complex optics and mechanisms of larger cameras can't be replaced with much more compact mobile device cameras and clever algorithms. (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.)ĭigital zoom using algorithms (rather than lenses) has long been the “ugly duckling” of mobile device cameras. Posted by Bartlomiej Wronski, Software Engineer and Peyman Milanfar, Lead Scientist, Computational Imaging ![]()
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