A learning-based approach to image demosaicking with spatial autocorrelation analysis

Min Kook Choi; Chan Joo; Hyun Gyu Lee; Sang Chul Lee

doi:10.2352/issn.2470-1173.2016.20.color-314

A learning-based approach to image demosaicking with spatial autocorrelation analysis

Min Kook Choi, Chan Joo, Hyun Gyu Lee, Sang Chul Lee

Inha University

Research output: Contribution to journal › Conference article › peer-review

Abstract

We introduce a two stage image demosaicking method for Bayer color filter array (CFA) images. Pixel interpolation using a Bayesian and/or SVM classifier is followed by renegotiation of the interpolated image with an auto-correlation function (ACF), which is applied to the distribution of edge strengths at each pixel of the interpolated image. This second stage can also be used to postprocess images produced by other demosaicking methods. Experimental results obtained with the Kodak PhotoCD benchmark show that our method shows enhanced edge and texture details and when compared with three other methods.

Original language	English
Journal	IS and T International Symposium on Electronic Imaging Science and Technology
DOIs	https://doi.org/10.2352/issn.2470-1173.2016.20.color-314
State	Published - 2016
Event	Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications 2016 - San Francisco, United States Duration: 14 Feb 2016 → 18 Feb 2016

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© 2016 Society for Imaging Science and Technology.

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10.2352/issn.2470-1173.2016.20.color-314

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title = "A learning-based approach to image demosaicking with spatial autocorrelation analysis",

abstract = "We introduce a two stage image demosaicking method for Bayer color filter array (CFA) images. Pixel interpolation using a Bayesian and/or SVM classifier is followed by renegotiation of the interpolated image with an auto-correlation function (ACF), which is applied to the distribution of edge strengths at each pixel of the interpolated image. This second stage can also be used to postprocess images produced by other demosaicking methods. Experimental results obtained with the Kodak PhotoCD benchmark show that our method shows enhanced edge and texture details and when compared with three other methods.",

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AU - Joo, Chan

AU - Lee, Hyun Gyu

AU - Lee, Sang Chul

PY - 2016

Y1 - 2016

N2 - We introduce a two stage image demosaicking method for Bayer color filter array (CFA) images. Pixel interpolation using a Bayesian and/or SVM classifier is followed by renegotiation of the interpolated image with an auto-correlation function (ACF), which is applied to the distribution of edge strengths at each pixel of the interpolated image. This second stage can also be used to postprocess images produced by other demosaicking methods. Experimental results obtained with the Kodak PhotoCD benchmark show that our method shows enhanced edge and texture details and when compared with three other methods.

AB - We introduce a two stage image demosaicking method for Bayer color filter array (CFA) images. Pixel interpolation using a Bayesian and/or SVM classifier is followed by renegotiation of the interpolated image with an auto-correlation function (ACF), which is applied to the distribution of edge strengths at each pixel of the interpolated image. This second stage can also be used to postprocess images produced by other demosaicking methods. Experimental results obtained with the Kodak PhotoCD benchmark show that our method shows enhanced edge and texture details and when compared with three other methods.

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DO - 10.2352/issn.2470-1173.2016.20.color-314

M3 - Conference article

AN - SCOPUS:85087226798

SN - 2470-1173

JO - IS and T International Symposium on Electronic Imaging Science and Technology

JF - IS and T International Symposium on Electronic Imaging Science and Technology

T2 - Color Imaging XXI: Displaying, Processing, Hardcopy, and Applications 2016

Y2 - 14 February 2016 through 18 February 2016

ER -

A learning-based approach to image demosaicking with spatial autocorrelation analysis

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