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Image-adaptive Color Super-resolution

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Abstract:

Image super-resolution is the problem of recovering a high resolution (hi-res) image from multiple low resolution (lo-res) acquisitions of a scene. The main focus and the most significant contributions of research in this area have been on the problem of super-resolving single channel (grayscale) images. Multi-channel (color) image super-resolution is often treated as an extension to grayscale super-resolution by simply considering the luminance component of the image more carefully than the chrominance components. In this paper we address explicitly the problem of color image super-resolution by formulating an optimization problem that leads to convergence guarantees. The key contribution of this work is the inclusion of a color regularizer that effectively accounts for both luminance and chrominance geometry in images. We show results demonstrating substantial image quality improvement over the state of the art, especially for images with significant chrominance geometry.

Document Type: Research Article

Publication date: 2011-01-01

More about this publication?
  • CIC is the premier annual technical gathering for scientists, technologists, and engineers working in the areas of color science and systems, and their application to color imaging. Participants represent disciplines ranging from psychophysics, optical physics, image processing, color science to graphic arts, systems engineering, and hardware and software development. While a broad mix of professional interests is the hallmark of these conferences, the focus is color. CICs traditionally offer two days of short courses followed by three days of technical sessions that include three keynotes, an evening lecture, and a vibrant interactive papers session. An endearing symbol of the meeting is the Cactus Award, given each year to the author(s) of the best interactive paper presentation.

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