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Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern.

Paul Oh1, Sukho Lee2, Moon Gi Kang3

  • 1Department of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-Ku, Seoul 03722, Korea. phoenix820@naver.com.

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Summary
This summary is machine-generated.

A novel RGB-White (RGBW) color filter array (CFA) uses a colorization algorithm for improved image quality. This new CFA design enhances signal-to-noise ratio (SNR), particularly in low light conditions, revealing details missed by conventional methods.

Keywords:
RGB-Whitecolor filter arraycolor interpolationcolorizationlow light conditionsrandomly sampled pattern

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Area of Science:

  • Image processing and computational photography.
  • Development of novel color filter array (CFA) designs.
  • Exploration of advanced color interpolation techniques.

Background:

  • Existing RGB-White (RGBW) color filter arrays (CFAs) typically convert data to Bayer patterns, limiting their potential.
  • The white (W) pixels in RGBW CFAs offer superior signal-to-noise ratio (SNR), especially in low light.
  • Conventional demosaicing methods struggle to fully leverage the benefits of high-sensitivity W pixels.

Purpose of the Study:

  • To propose a new RGBW color filter array (CFA) design.
  • To introduce a novel color interpolation technique based on the colorization algorithm for RGBW CFAs.
  • To enhance image quality and low-light performance by maximizing the use of W pixels.

Main Methods:

  • Designed a new RGBW color filter array (CFA) with a high proportion of W pixels and randomly distributed RGB pixels.
  • Adapted the colorization algorithm, initially for grayscale image colorization, as a color interpolation method for the proposed CFA.
  • Evaluated the performance of the new CFA and interpolation technique against conventional methods.

Main Results:

  • The proposed RGBW CFA, utilizing the colorization algorithm, enables reconstruction of color images from a limited number of RGB values.
  • Images reconstructed with the proposed method exhibit a significantly higher signal-to-noise ratio (SNR), especially in low light conditions.
  • Crucial image details, often imperceptible with conventional CFAs, are successfully recovered.

Conclusions:

  • The novel RGBW CFA design combined with the colorization algorithm offers a superior approach to image reconstruction.
  • This method effectively exploits the high sensitivity of W pixels, leading to improved image quality in challenging lighting.
  • The technique demonstrates potential for capturing more visual information compared to traditional CFA and demosaicing methods.