• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.344-349
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• 2012

This paper describes experimental results and their analysis on the new test images, called as the McMaster image dataset, to develop demosaicking techniques. The well-known image dataset for demosaicking is so far the Kodak image dataset. However, different results have been reported, as the new image dataset is engaged in developing demosaicking techniques. Thus, we conduct a series of experiments on both the McMaster dataset and the Kodak dataset; we analyze and compare those experimental results; and we provide the peculiar features of the new dataset. Also, the experimental results and their analysis indicate that the McMaster dataset deserves to be a test image dataset for future demosaicking techniques; thus, we expect they can be utilized as basic data for demosaicking.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.10
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• pp.1434-1440
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• 2014

This paper describes a demosaicking method for hexagonally-structured color filter array. Demosaicking is essential to acquire color images using color filter array (CFA) in single sensor imaging. Thus, CFA patterns have been discussed in order to improve image quality in single sensor imaging after the Bayer pattern are introduced. Advancements in imaging sensor technology recently introduce a hexagonal CFA pattern. The hexagonal CFA can be considered to be a 45-degree rotational version of the Bayer pattern, thus demosaicking can be implemented by an existing method with backward and forward 45-degree rotations. However, this approach requires heavy computing power and memory in image sensing devices because of the image rotations. To overcome this problem, we proposes a demosaicking method for a hexagonal Bayer CFA without rotations. In addition, we introduce a weighting parameter in our demosaicking method to improve image quality and to unifying exiting method with our method. Experimental results indicate that the proposed method is superior to conventional methods in terms of PSNR. In addition, some optimized values for the weighting parameter are provided experimentally.