PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras
IEEE Transactions on Image Processing
Demosaicing: image reconstruction from color CCD samples
IEEE Transactions on Image Processing
Color plane interpolation using alternating projections
IEEE Transactions on Image Processing
Color filter array demosaicking: new method and performance measures
IEEE Transactions on Image Processing
Primary-consistent soft-decision color demosaicking for digital cameras (patent pending)
IEEE Transactions on Image Processing
Adaptive homogeneity-directed demosaicing algorithm
IEEE Transactions on Image Processing
Linear demosaicing inspired by the human visual system
IEEE Transactions on Image Processing
Color demosaicking via directional linear minimum mean square-error estimation
IEEE Transactions on Image Processing
Color Demosaicing Using Variance of Color Differences
IEEE Transactions on Image Processing
Demosaicing With Directional Filtering and a posteriori Decision
IEEE Transactions on Image Processing
Heterogeneity-Projection Hard-Decision Color Interpolation Using Spectral-Spatial Correlation
IEEE Transactions on Image Processing
Color Reproduction From Noisy CFA Data of Single Sensor Digital Cameras
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Effective color interpolation in CCD color filter arrays using signal correlation
IEEE Transactions on Circuits and Systems for Video Technology
Demosaicked image postprocessing using local color ratios
IEEE Transactions on Circuits and Systems for Video Technology
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An edge adaptive color demosaicking algorithm that classifies the region types and estimates the edge direction on the Bayer color filter array (CFA) samples is proposed. In the proposed method, the optimal edge direction is estimated based on the spatial correlation on the Bayer color difference plane, which adopts the local directional correlation of an edge region of the Bayer CFA samples. To improve the image quality with the consistent edge direction, we classify the region of an image into three different types, such as edge, edge pattern, and flat regions. Based on the region types, the proposed method estimates the edge direction adaptive to the regions. As a result, the proposed method reconstructs clear edges with reduced visual distortions in the edge and the edge pattern regions. Experimental results show that the proposed method outperforms conventional edge-directed methods on objective and subjective criteria.