Next Article in Journal
Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments
Previous Article in Journal
Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier
Previous Article in Special Issue
Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China
Open AccessArticle

Demosaicing of CFA 3.0 with Applications to Low Lighting Images

Applied Research LLC; Rockville, MD 20850, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3423; https://doi.org/10.3390/s20123423
Received: 25 April 2020 / Revised: 1 June 2020 / Accepted: 15 June 2020 / Published: 17 June 2020
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. View Full-Text
Keywords: debayering; demosaicing; color filter array (CFA); RGBW pattern; Bayer pattern; CFA 1.0; CFA 2.0; CFA 3.0; pansharpening; deep learning debayering; demosaicing; color filter array (CFA); RGBW pattern; Bayer pattern; CFA 1.0; CFA 2.0; CFA 3.0; pansharpening; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Kwan, C.; Larkin, J.; Ayhan, B. Demosaicing of CFA 3.0 with Applications to Low Lighting Images. Sensors 2020, 20, 3423.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop