Effective Three-Stage Demosaicking Method for RGBW CFA Images Using The Iterative Error-Compensation Based Approach

Area of Science:

• Image Processing
• Computer Vision

Background:

• RGBW color filter arrays (CFAs) offer superior luminance information compared to traditional Bayer CFAs.
• Demosaicking is crucial for converting RGBW CFA images into high-quality RGB full-color images for human perception.

Discussion:

• A novel three-stage demosaicking method is proposed for RGBW CFA images.
• The first stage uses a cross-shape color difference approach to interpolate missing white pixels.
• The second stage employs iterative error compensation for improved demosaicked image quality.
• The third stage refines the image using the input RGBW CFA as ground truth.

Key Insights:

• The proposed method achieves substantial quality and perceptual improvements.
• Experimental results demonstrate superior performance against Hamilton and Compton, Kwan et al.'s pansharpening, and Kwan and Chou's deep learning methods.
• The method is validated on Kodak and IMAX datasets.

Outlook:

• Further research could explore real-time applications of this demosaicking technique.
• Optimization for different CFA patterns and noise conditions may enhance its versatility.
• Integration with advanced deep learning architectures could yield even greater performance gains.