Practical implementation of LMMSE demosaicing using luminance and chrominance spaces
Computer Vision and Image Understanding
A new edge-adaptive demosaicing algorithm for color filter arrays
Image and Vision Computing
Additive spread-spectrum watermark detection in demosaicked images
Proceedings of the 11th ACM workshop on Multimedia and security
IEEE Transactions on Image Processing
Robust color demosaicking with adaptation to varying spectral correlations
IEEE Transactions on Image Processing
Adaptive color filter array demosaicking based on constant hue and local properties of luminance
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
An effective edge-adaptive color demosaicking algorithm for single sensor digital camera images
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Optimal color spaces for image demosaicing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Effective demosaicking algorithm based on edge property for color filter arrays
Digital Signal Processing
High quality color interpolation for color filter array with low complexity
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
A Hand-held Mosaicked Multispectral Imaging Device for Early Stage Pressure Ulcer Detection
Journal of Medical Systems
Classification-based de-mosaicing for digital cameras
Neurocomputing
On a generalized demosaicking procedure: a taxonomy of single-sensor imaging solutions
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Color demosaicking with an image formation model and adaptive PCA
Journal of Visual Communication and Image Representation
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To minimize cost and size, most commercial digital cameras acquire imagery using a single electronic sensor (CCD or CMOS) overlaid with a color filter array (CFA) such that each sensor pixel only samples one of the three primary color values. To restore a full-color image from CFA samples, the two missing color values at each pixel need to be estimated from the neighboring samples, a process that is commonly known as CFA demosaicking or interpolation. In this paper we present two contributions to CFA demosaicking. First, we stress the importance of well exploiting both image spatial and spectral correlations, and characterize the demosaicking artifacts due to inadequate use of either correlation. Second, based on the insights gained from our empirical study, we propose effective schemes to enhance two existing state-of-the-art demosaicking methods. Experimental results show that our enhanced methods achieve notable improvements over the existing methods, in terms of both subjective and objective evaluations, on a large variety of test images. In addition, the computational complexities of the enhanced methods are comparable to the originals.