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Chromatic Aberration Correction and Spectral Reconstruction from Colour Images
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- Author / Creator
- Llanos, Bernard
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We present an algorithm for simultaneously demosaicing digital images, and correcting chromatic aberration, that operates in a latent space of spectral bands. Light refraction by a camera lens system depends on the wavelength of the light, causing relative shifting, and blurring, between intensity patterns in different wavelengths on the image sensor. The effect on the image is called chromatic aberration, and appears as colour fringes around edges in the image, and blur.
Chromatic aberration depends not only on the camera's optical system, but also on the spectral characteristics of the light entering the camera. Previous works on calibrating chromatic aberration produce models of chromatic aberration that assume fixed discrepancies between image channels, an assumption that is only valid when the image channels capture narrow regions of the electromagnetic spectrum. When the camera has wideband channels, as is the case for conventional trichromatic (RGB) cameras, the aberration observed both within and between channels can only be accurately predicted given the spectral irradiance of the theoretical, aberration-free image.
We develop a physically-correct chromatic aberration calibration procedure for RGB cameras. Using bandpass-filtered light, we calibrate a model of chromatic aberration as an image distortion that is parameterized by both image position, and light wavelength. To correct chromatic aberration, we estimate a spectral image that corresponds to the RGB image by solving a global numerical optimization problem. We include our model of chromatic aberration in the data-fitting term of the optimization problem that models the transformation from the spectral image to the captured RGB image. We also include regularization terms in our optimization problem to enforce smoothness in the output image. Whereas the captured RGB image is mosaiced, meaning that each pixel senses only one colour channel, our algorithm does not require a demosaicing preprocessing step to recover all colour channel intensities at each pixel. Therefore, we avoid introducing bias from demosaicing algorithms, which is important because chromatic aberration and demosaicing are known to interact.
Since we model within-channel chromatic aberration, our reconstructed images are sharper than those obtained by previous works on calibrated warping of colour channels. In contrast to explicit deblurring algorithms, our algorithm leaves defocus blur intact, separating it from chromatic aberration. We also avoid introducing artifacts, such as ringing, that are commonly produced by deblurring algorithms. Nevertheless, recovering spectral images from RGB images is an ill-posed problem, and this ill-posedness is the major limitation of our approach. We determined that our spectral images have higher accuracy than measurements made using a consumer-grade spectrometer. Still, we recommend further research on RGB-to-spectral reconstruction, especially in relation to chromatic aberration, which may serve as useful constraint.
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- Subjects / Keywords
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- Graduation date
- Fall 2019
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.