Chromatic interpolation based on anisotropy-scale-mixture statistics

We present an efficient approach for reconstructing full-color images from imagery data acquired using a color filter array (CFA). On the basis of our understanding of early visual processing in humans, we utilize a correlation among the multi-scale directional-anisotropy statistics observed in natural images in order to estimate the missing high-spatial-frequency chromatic signals. The directional anisotropy in the high-spatial-frequency component is efficiently and robustly estimated according to a mixture of anisotropy statistics derived from lower-frequency components. We show that, in spite of its simple implementation, our proposed method gives an excellent numerical performance as well as a perceptually natural output. The present approach is expected to provide a promising methodology for the on-line processing of recently rising high-resolution image data acquired using a single-sensor CFA.