Additive spread-spectrum watermark detection in demosaicked images
Proceedings of the 11th ACM workshop on Multimedia and security
Regularization approaches to demosaicking
IEEE Transactions on Image Processing
Self-similarity driven color demosaicking
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Color filter array design using random patterns with blue noise chromatic spectra
Image and Vision Computing
A new color filter array with optimal sensing properties
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Joint demosaicking and denoising with space-varying filters
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Demosaicking by alternating projections: theory and fast one-step implementation
IEEE Transactions on Image Processing
Color image demosaicking: An overview
Image Communication
Novel color demosaicking for noisy color filter array data
Signal Processing
Color texture analysis using CFA chromatic co-occurrence matrices
Computer Vision and Image Understanding
Discrete wavelet transform on color picture interpolation of digital still camera
VLSI Design - Special issue on Advanced VLSI Design Methodologies for Emerging Industrial Multimedia and Communication Applications
Chromatic interpolation based on anisotropy-scale-mixture statistics
Signal Processing
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Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and 2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.