scholarly journals Reversible color transform for Bayer color filter array images

2013 ◽  
Vol 2 ◽  
Author(s):  
Suvit Poomrittigul ◽  
Masanori Ogawa ◽  
Masahiro Iwahashi ◽  
Hitoshi Kiya
Keyword(s):  
Color Images ◽  
Color Filter ◽  
Statistical Relation ◽  
Color Filter Array ◽  
Computational Load ◽  
Bayer Pattern ◽  
Filter Array ◽  
Color Transform ◽  
Karhunen Loeve Transform ◽  
Color Components

In this paper, we propose a reversible color transform (RCT) for color images acquired through a Bayer pattern color filter array. One existing RCT with fixed coefficients is simple to implement. However, it is not adaptive to each of input images. Another existing RCT based on eigenvector of covariance matrix of color components, which is equivalent to Karhunen–Loève transform (KLT), is adaptive. However, it requires heavy computational load. We remove a redundant part of this existing method, utilizing fixed statistical relation between two green components at different locations. Comparing to the KLT-based existing RCT, it was observed that the proposed RCT keeps adaptability and has better coding performance, even though its computational load is reduced.

scholarly journals Demosaicing of CFA 3.0 with Applications to Low Lighting Images

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3423 ◽  
Author(s):  
Chiman Kwan ◽  
Jude Larkin ◽  
Bulent Ayhan
Keyword(s):  
Comparative Study ◽  
Poisson Noise ◽  
Color Filter ◽  
Signal To Noise ◽  
Color Filter Array ◽  
Lighting Conditions ◽  
Bayer Pattern ◽  
Filter Array ◽  
Better Than

Low lighting images usually contain Poisson noise, which is pixel amplitude-dependent. More panchromatic or white pixels in a color filter array (CFA) are believed to help the demosaicing performance in dark environments. In this paper, we first introduce a CFA pattern known as CFA 3.0 that has 75% white pixels, 12.5% green pixels, and 6.25% of red and blue pixels. We then present algorithms to demosaic this CFA, and demonstrate its performance for normal and low lighting images. In addition, a comparative study was performed to evaluate the demosaicing performance of three CFAs, namely the Bayer pattern (CFA 1.0), the Kodak CFA 2.0, and the proposed CFA 3.0. Using a clean Kodak dataset with 12 images, we emulated low lighting conditions by introducing Poisson noise into the clean images. In our experiments, normal and low lighting images were used. For the low lighting conditions, images with signal-to-noise (SNR) of 10 dBs and 20 dBs were studied. We observed that the demosaicing performance in low lighting conditions was improved when there are more white pixels. Moreover, denoising can further enhance the demosaicing performance for all CFAs. The most important finding is that CFA 3.0 performs better than CFA 1.0, but is slightly inferior to CFA 2.0, in low lighting images.

A Research on X-Trans Patterned CMOS

2013 ◽  
Vol 705 ◽  
pp. 313-318
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Spatial Resolution ◽  
Digital Camera ◽  
Super Resolution ◽  
Experimental Results ◽  
Color Filter ◽  
Color Filter Array ◽  
Bayer Pattern ◽  
Filter Array

In the last two decades, super-resolution and demosaicking have been researched actively. Generally, digital camera suffers from both color filtering and low spatial resolution. Therefore it is worth studying above issues. In this paper, we design filters to conduct super-resolution for digital camera. Bayer pattern has been widely used in digital camera. In this paper, we conduct experiments on X-Trans color filter array (CFA) pattern. Experimental results confirm that the proposed filters are effective for solving demosaicking issues.

Review of Bayer Pattern Color Filter Array (CFA) Demosaicing with New Quality Assessment Algorithms

2010 ◽  
Author(s):  
Jr Maschal ◽  
Young Robert A. ◽  
Reynolds S. S. ◽  
Krapels Joe ◽  
Fanning Keith ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Color Filter ◽  
Color Filter Array ◽  
Bayer Pattern ◽  
Filter Array

Compression of noisy Bayer pattern color filter array images

2007 ◽  
Author(s):  
Vladimir V. Lukin ◽  
Nikolay N. Ponomarenko ◽  
Andriy V. Bazhyna ◽  
Karen O. Egiazarian
Keyword(s):  
Color Filter ◽  
Color Filter Array ◽  
Bayer Pattern ◽  
Filter Array
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