We propose a channel-correlation adaptive dictionary learning based demosaicking (CADLD).
We propose a fully automatic approach to designing high-sensitivity CFAs using panchromatic pixels based on a mathematical model.
In this paper, we consider optimally designing CFAs for sparse representation-based demosaicking, where the dictionary is well-chosen.
We present a CFA design methodology in the frequency domain. The frequency structure, which is shown to be just the symbolic DFT of the CFA pattern (one period of the CFA), is introduced to represent im- ages sampled with any rectangular CFAs in the frequency domain.
This article introduces the frequency structure matrix as a new representation of color filter arrays (CFAs).