Estimation of Bayer CFA pattern configuration based on singular value decomposition

Abstract

Abstract An image sensor can measure only one color per pixel through the color filter array. Missing pixels are estimated using an interpolation process. For this reason, a captured pixel and interpolated pixel have different statistical characteristics. Because the pattern of a color filter array is changed when the image is manipulated or forged, this pattern change can be a clue to detect image forgery. However, the majority of forgery detection algorithms assume that they know the color filter array pattern. Therefore, estimating the configuration of the color filter array can have an important role as a precondition for image forgery detection. In this paper, we propose an efficient algorithm for estimating the Bayer color filter array configuration. We first construct a color difference image to reflect the characteristics of different demosaicing methods. To identify the color filter array pattern, we employ singular value decomposition. The truncated sum of the singular values is used to distinguish the color filter array pattern. Experimental results confirm that the proposed method generates acceptable estimation results in identifying color filter array patterns. Compared with conventional methods, the proposed method provides superior performance.

Keywords

• Color filter array
• Bayer pattern identification
• Forgery detection
• Singular value
• Color difference image
• Demosaicing