Missing image interpolation using sigma-delta modulation type of DT-CNN

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This paper proposes a new interpolation method for an incomplete image using sigma-delta modulation type of Discrete-Time Cellular Neural Networks. Missing pixels in an image are interpolated by function of its nearest values using B-template with Gaussian filter. We can reconstruct analog image which has missing values into digital image by using this framework. We evaluated our new proposed method with six standard images which have missing pixels at various percentages of missing values. The experimental results show that, by using sigma-delta modulation type of Discrete-Time Cellular Neural Networks, we can achieve a high peak signal-to-noise ratio for various image datasets and at different rates of missingness.

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