Proposed Algorithm Overview
Abstract
Multispectral demosaicking, which estimates full multispectral images from raw data observed using a single image sensor with a color filter array (CFA), is a challenging task because each spectral component is severely undersampled. In this paper, we propose a novel multispectral demosaicking algorithm. We extend existing upsampling algorithms to adaptive kernel upsampling algorithms using an adaptive kernel as a spatial weight and apply them to multispectral demosaicking. We also propose a new CFA and a direct adaptive kernel estimation from the raw data of the proposed CFA. Experimental results with real multispectral images demonstrate the effectiveness of the proposed algorithm.
References
- Multispectral Demosaicking Using Adaptive Kernel Upsampling
Yusuke Monno, Masayuki Tanaka, and Masatoshi Okutomi
IEEE International Conference on Image Processing (ICIP2011), pp.3218-3221, September, 2011. [PDF]
Dataset
Results
Comparison with other multispectral demosaicking algorithms
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| Original image | BTES [1] |
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| N-UP | Proposed |
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| Original image | BTES [1] |
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| N-UP | Proposed |
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| Original image | BTES [1] |
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| N-UP | Proposed |
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| Original image | BTES [1] |
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| N-UP | Proposed |
Comparison with Bayer demosaicking algorithms
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References
[1] L. Miao, H. Qi, R. Ramanath and W. E. Snyder, "Binary tree-based generic demosaicking algorithm for multispectral filter arrays," IEEE Transactions on Image Processing, vol.15, pp.3550-3558, 2006.
[2] K. Hirakawa and T. W. Parks, "Adaptive homogeneity-directed demosaicking algorithm," IEEE Transactions on Image Processing, vol.14, pp.360-369, 2005.
[3] D.Paliy, V. Katkovnik, R. Bilcu, S. Alenius and K. Egiazarian, "Spatially adaptive color filter array interpolation for noiseless and noisy data," International Journal of Imaging Systems and Technology, vol.17, pp.105-122, 2007.