In this paper, a refinement scheme considering channel correlation is presented for a color filter array (CFA) sensor with the white (W) channel. Differently from the Bayer CFA, which has the red, green, and blue (R, G, and B) channels only, the R, G, B, and W channels can be alternately assigned to each grid to form new CFA patterns. However, the resolution degradation of the interpolated results in the pattern with the R, G, B, and W channels is more prominent than that in the Bayer pattern, because channel correlated errors in the high frequency, which originate from errors during the color interpolation process, such as false color and aliasing artifacts along the edges and in the details, are magnified. The proposed refinement scheme is applied to the three CFA patterns, which contain the W channel of the quincuncial structure. The interpolated W channel has more high frequency than the other interpolated R, G, and B channels, because the W channel occupies the largest pixel samples in the patterns. Thus, the W channel is eventually utilized to improve the R, G, and B channels by applying the edge adaptive refinement considering channel correlation based on high frequency reconstruction. First, the CFA patterns are partially interpolated to generate quincuncial patterns with the same structures of the Bayer pattern. Then, the edge adaptive color interpolation is applied to each pattern. Finally, the smoothing filter based on robust estimator is adopted for the refinement of the color difference image between the degraded color channel and the high resolution W channel. Experimental results of the proposed scheme are shown in comparison with the results of the conventional methods.
Bibliographical noteFunding Information:
This work was supported by the Mid-career Researcher Program through a NRF grant funded by the MEST (No. 2012R1A2A4A01003732 ).
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering