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Open Access Colorizing Color Images

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This paper describes a method of improving the quality of the color in color images by colorizing them. In particular, color quality may suffer from improper white balance and other factors such as inadequate camera characterization. Colorization generally refers to the problem of turning a luminance image into a realistic looking color image and impressive results have been reported in the computer vision literature. Based on the assumption that if colorization can successfully predict colors from luminance data alone then it should certainly be able to predict colors from color data, the proposed method employs colorization to 'color' color images. Tests show that the proposed method quite effectively removes color casts—including spatially varying color casts—created by changes in the illumination. The colorization method itself is based on training a deep neural network to learn the connection between the colors in an improperly balanced image and those in a properly balanced one. Unlike many traditional white-balance methods, the proposed method is image-in-image-out and does not explicitly estimate the chromaticity of the illumination nor apply a von-Kries-type adaptation step. The colorization method is also spatially varying and so handles spatially varying illumination conditions without further modification.
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Keywords: CAMERA CHARACTERIZATION; COLOR QUALITY; COLORIZING; DEEP NEURAL NETWORK; SPATIALLY VARYING COLOR CORRECTION; WHITE BALANCE

Document Type: Research Article

Publication date: 28 January 2018

This article was made available online on 28 January 2018 as a Fast Track article with title: "Colorizing color images".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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