Color Filter Array Interpolation Using Cellular Neural Networks Considering Self-Congruence

Conference: CNNA 2018 - The 16th International Workshop on Cellular Nanoscale Networks and their Applications
08/28/2018 - 08/30/2018 at Budapest, Hungary

Proceedings: CNNA 2018

Pages: 4Language: englishTyp: PDF

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Iriyama, Taishi; Otake, Tsuyoshi (Graduate School of Engineering, Tamagawa University, Tokyo, Japan)
Sato, Masatoshi (Department of Software Science, Tamagawa University, Tokyo, Japan)
Aomori, Hisashi (Department of Information System Technology, Chukyo University, Aichi, Japan)
Tanaka, Mamoru (Department of Information and Communication Sciences, Sophia University, Tokyo, Japan)

In this paper, we propose a novel color filter array (CFA) interpolation technique based on color difference correlation model. The difference model exploits the interchannel correlation, where the interpolation is achieved by using the color differences R-G and B-G. At first the green channel is handled, and the other color channels are estimated based on the result of green channel. Missing color difference values are estimated by using discrete-time cellular neural network (DTCNN) predictor. First, the DT-CNN transforms color difference values into the optimal coefficients which make possible to establish the optimal prediction using the quincunx A-template. Then, the optimal missing color differences are obtained by using the convolution of the B-template which is derived by rotating the A-template. Moreover, we utilize self-congruence property in order to improve the prediction performance around edges. Experimental evaluation shows that the proposed method has a better performance compared with the conventional method.