Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor
Recently, several red-green-blue-white (RGBW) color filter arrays (CFAs), which include highly sensitive W pixels, have been proposed. However, RGBW CFA patterns suffer from spatial resolution degradation owing to the sensor composition having more color components than the Bayer CFA pattern. RGBW C...
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doaj-054025f2adcb40f8a9598bf0bce6a4c52020-11-24T22:06:43ZengMDPI AGSensors1424-82202018-05-01185164710.3390/s18051647s18051647Sensitivity and Resolution Improvement in RGBW Color Filter Array SensorSeunghoon Jee0Ki Sun Song1Moon Gi Kang2Department of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei Road, Seodaemun-gu, Seoul 03722, KoreaDepartment of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei Road, Seodaemun-gu, Seoul 03722, KoreaDepartment of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei Road, Seodaemun-gu, Seoul 03722, KoreaRecently, several red-green-blue-white (RGBW) color filter arrays (CFAs), which include highly sensitive W pixels, have been proposed. However, RGBW CFA patterns suffer from spatial resolution degradation owing to the sensor composition having more color components than the Bayer CFA pattern. RGBW CFA demosaicing methods reconstruct resolution using the correlation between white (W) pixels and pixels of other colors, which does not improve the red-green-blue (RGB) channel sensitivity to the W channel level. In this paper, we thus propose a demosaiced image post-processing method to improve the RGBW CFA sensitivity and resolution. The proposed method decomposes texture components containing image noise and resolution information. The RGB channel sensitivity and resolution are improved through updating the W channel texture component with those of RGB channels. For this process, a cross multilateral filter (CMF) is proposed. It decomposes the smoothness component from the texture component using color difference information and distinguishes color components through that information. Moreover, it decomposes texture components, luminance noise, color noise, and color aliasing artifacts from the demosaiced images. Finally, by updating the texture of the RGB channels with the W channel texture components, the proposed algorithm improves the sensitivity and resolution. Results show that the proposed method is effective, while maintaining W pixel resolution characteristics and improving sensitivity from the signal-to-noise ratio value by approximately 4.5 dB.http://www.mdpi.com/1424-8220/18/5/1647red-green-blue-white (RGBW)demosaicingtexture decompositioncolor filter arraysensitivity improvementresolution improvement |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seunghoon Jee Ki Sun Song Moon Gi Kang |
spellingShingle |
Seunghoon Jee Ki Sun Song Moon Gi Kang Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor Sensors red-green-blue-white (RGBW) demosaicing texture decomposition color filter array sensitivity improvement resolution improvement |
author_facet |
Seunghoon Jee Ki Sun Song Moon Gi Kang |
author_sort |
Seunghoon Jee |
title |
Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor |
title_short |
Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor |
title_full |
Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor |
title_fullStr |
Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor |
title_full_unstemmed |
Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor |
title_sort |
sensitivity and resolution improvement in rgbw color filter array sensor |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-05-01 |
description |
Recently, several red-green-blue-white (RGBW) color filter arrays (CFAs), which include highly sensitive W pixels, have been proposed. However, RGBW CFA patterns suffer from spatial resolution degradation owing to the sensor composition having more color components than the Bayer CFA pattern. RGBW CFA demosaicing methods reconstruct resolution using the correlation between white (W) pixels and pixels of other colors, which does not improve the red-green-blue (RGB) channel sensitivity to the W channel level. In this paper, we thus propose a demosaiced image post-processing method to improve the RGBW CFA sensitivity and resolution. The proposed method decomposes texture components containing image noise and resolution information. The RGB channel sensitivity and resolution are improved through updating the W channel texture component with those of RGB channels. For this process, a cross multilateral filter (CMF) is proposed. It decomposes the smoothness component from the texture component using color difference information and distinguishes color components through that information. Moreover, it decomposes texture components, luminance noise, color noise, and color aliasing artifacts from the demosaiced images. Finally, by updating the texture of the RGB channels with the W channel texture components, the proposed algorithm improves the sensitivity and resolution. Results show that the proposed method is effective, while maintaining W pixel resolution characteristics and improving sensitivity from the signal-to-noise ratio value by approximately 4.5 dB. |
topic |
red-green-blue-white (RGBW) demosaicing texture decomposition color filter array sensitivity improvement resolution improvement |
url |
http://www.mdpi.com/1424-8220/18/5/1647 |
work_keys_str_mv |
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