“Everyday the web is used to share and store millions of pictures, enabling one to explore the world, research new topics of interest, or even share a vacation with friends and family. However, many of these images are either limited by the resolution of the device used to take the picture, or purposely degraded in order to accommodate the constraints of cell phones, tablets, or the networks to which they are connected. With the ubiquity of high-resolution displays for home and mobile devices, the demand for high-quality versions of low-resolution images, quickly viewable and shareable from a wide variety of devices, has never been greater. With “RAISR: Rapid and Accurate Image Super-Resolution”, we introduce a technique that incorporates machine learning in order to produce high-quality versions of low-resolution images. RAISR produces results that are comparable to or better than the currently available super-resolution methods, and does so roughly 10 to 100 times faster, allowing it to be run on a typical mobile device in real-time. Furthermore, our technique is able to avoid recreating the aliasing artifacts that may exist in the lower resolution image. “
Related Content
Related Posts:
- Meet Nuvem, a cable to connect Portugal, Bermuda, and the U.S.
- Our progress toward quantum error correction
- OK Google, get me a Coke: AI giant demos soda-fetching robots
- Even more pi in the sky: Calculating 100 trillion digits of pi on Google Cloud
- A New Library for Network Optimization
- Open source SystemVerilog tools in ASIC design
- Our Grace Hopper subsea cable has landed in the UK
- Hola, South America! Announcing the Firmina subsea cable
- Unveiling our new Quantum AI campus
- The Dunant subsea cable, connecting the US and mainland Europe, is ready for service