Ilan Yona, Director of Imaging and Computer Vision at CEVA, presents the "Efficient Super-Resolution Algorithms and Implementation Techniques for Constrained Applications" tutorial within the "Front-End Image Processing for Vision Applications" technical session at the October 2013 Embedded Vision Summit East.

Image quality is a critical challenge in many applications, including smart phones, especially when using low quality sensors or when using digital zoom for enlarging part of the image. Super-resolution is a set of techniques that can address this challenge by combining multiple images to produce a single, higher quality image. However, super-resolution can be extremely computationally demanding, so when implementing it on a constrained platform (such as a smart phone), the algorithm should be carefully chosen, balancing image quality, speed, and power consumption.

CEVA tested variety of known super-resolution algorithms and found that they were not efficient for cost- and power-constrained systems. The company then developed a new algorithm that produces good quality images and is suitable for constrained systems. In this talk, Ilan Yona explains how super-resolution works, introduces the previously known algorithms, and presents CEVA's new algorithm and a sample implementation of it.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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