Demosaicking methods for Bayer color arrays

By R. Ramanath, W. Snyder, G. Bilbro, W. Sander. J. Electronic Imaging, 2002

332 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/28c899ab34b6dd91d10474b5635eec6a97b8e3fa

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

In Industrial and Manufacturing Engineering

Citations in the corpus

Noise Estimation from a Single Image (2006, background): “Real CCD noise is not white, however; there are spatial correlations introduced by “demosaicing” [13], i.”
Multiframe demosaicing and super-resolution of color images (2006, background): “cameras [ 36 ], so each LR image may be thought of as one picture taken with this camera brand.”
Exposing digital forgeries in color filter array interpolated images (2005, methodology): “…and propose a method that quantifies and detects them in any portion of an image.2 We show the general effectiveness of this technique in detecting traces of digital tampering and analyze its sensitivity and robustness to simple image distortions (compression, noise, and gamma correction).”
Visual odometry (2004, methodology): “The most commonly used filter mask is the Bayer color filter and it uses the human eye as a prototype [11, 15].”
High-quality linear interpolation for demosaicing of Bayer-patterned color images (2004, methodology): “Improved performance can be obtained with gradient-based methods [2],[6], which typically estimate edge directions and adjust the interpolation formulas so that filtering is performed preferentially along edge directions, and not across them.”
Advances and challenges in super‐resolution (2004, methodology): “Figure 6(b) shows one of these images demosaiced by the method of Laroche and Prescott (1994), which is employed in Kodak DCS-200 digital cameras (Ramanath et al., 2002).”
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition (2012, methodology): “All extracted traffic sign images are converted into RGB color images employing an edge-adaptive, constant-hue demosaicking method (Gunturk et al., 2005; Ramanath et al., 2002).”