By Zhiyu Chen, Andreas Koschan, Chung-Hao Chen (auth.), M. Emre Celebi, Bogdan Smolka (eds.)
Эта серьёзная книга рассказывает о фундаментальных аспектах работы с цветом в компьютерных системах. The objective of this quantity is to summarize the cutting-edge within the early levels of the colour snapshot processing pipeline. The meant viewers contains researchers and practitioners, who're more and more utilizing colour and, commonly, multichannel pictures. Contents 1. computerized colour Misalignment Correction for Close-Range and Long-Range Hyper-Resolution Multi-Line CCD pictures 2. Adaptive Demosaicing set of rules utilizing features of the colour clear out Array development three. A Taxonomy of colour fidelity and Invariance set of rules four. at the von Kries version: Estimation, Dependence on mild and equipment, and functions five. Impulse and combined Multichannel Denoising utilizing Statistical Halfspace intensity services 6. Spatially Adaptive colour photo Processing 7. Vector Ordering and Multispectral Morphological picture Processing eight. Morphological Template Matching in colour photographs nine. Tensor balloting for powerful colour aspect Detection 10. colour Categorization types for colour snapshot Segmentation eleven. pores and skin Detection and Segmentation in colour pictures 12. Contribution of dermis colour Cue in Face Detection functions thirteen. colour Saliency review for online game layout
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IEEE Trans Pattern Anal Mach Intell 19(9):963–975 2. Boyle W, Smith G (1974) Three dimensional charge coupled devices. US Patent 3,796,927, 1 Mar 1974 3. Boyle W, Smith G (1974) Buried channel charge coupled devices. US Patent 3,792,322, 24 Oct 24 1974 4. Janesick J, (2001) Scientific charge-coupled devices. SPIE Press, pp. 4, ISBN 978-0-81943698-6 5. Boyle W, Smith G (1970) Charge coupled semiconductor devices. Bell Sys Tech J 49(4):587– 593 6. Blanc N (2001) CCD versus CMOS—has CCD imaging come to an end?
1a, only with R p,q and B p,q interchanged. Thus, with the CFA pattern of Fig. 1b, unknown G pixel value Gˆ i, j is estimated using (1)–(3), only with R p,q replaced by B p,q . Figure 1c shows the Bayer CFA pattern at G center pixel with unknown R and B pixel values. The unknown R and B pixel values are estimated using the interpolated G pixel values, under the assumption that the high-frequency components have the similarity across three color components, R, G, and B. To evaluate the performance of the proposed algorithm, six conventional demosaicing algorithms [8, 9, 11, 15, 16, 20] are simulated.
These artifacts appear mainly in the high-frequency region. Therefore, the demosaiced image needs to be refined using the high-frequency components with inter-channel correlation of the R/G/B channels. For example, G components are updated by adding the highfrequency components in R (B) channel, which is the known (measured) pixel value in the CFA pattern. For example, in Fig. 1a where an R pixel is at the center of the 5×5 Bayer CFA pattern, G/B components are refined. The refined G component Gˆ is expressed as (29) Gˆ i, j = G i,→ j + Ri,h j Ri,h j = Ri, j − Ri,l j (30) where G i,→ j represents the interpolated G component at (i, j) at the adaptive interpolation step, Ri,h j is the high-frequency component of the R channel, and Ri,l j denotes the Adaptive Demosaicing Algorithm 41 low-frequency component that is filtered by a 3-tap 1-D filter [1/3, 1/3, 1/3] along the detected horizontal or vertical edge direction.
Advances in Low-Level Color Image Processing by Zhiyu Chen, Andreas Koschan, Chung-Hao Chen (auth.), M. Emre Celebi, Bogdan Smolka (eds.)