Highly effective iterative demosaicing using weighted-edge and color-difference interpolations

研究成果: 雜誌貢獻文章

43 引文 (Scopus)

摘要

Demosaicing is a process of obtaining a fullcolor image by interpolating the missing colors of an image captured from a single sensor color filter array. This paper provides an effective and low-complexity iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color-difference interpolation to update red, blue, and green pixels. Based on our experiments of images, we enable the algorithm a well-designed stopping condition and predetermine the proper weights of interpolation. Experimental results show that the proposed method performs much better than three state-of-the-art demosaicing techniques in terms of both computational cost and image quality. In comparison to the algorithm of successive approximation, the algorithm proposed here reduces mean squared error up to 14.5% while requiring computational cost only 22% on average. That is, it takes less time but performs better.

原文英語
頁(從 - 到)639-645
頁數7
期刊IEEE Transactions on Consumer Electronics
52
發行號2
DOIs
出版狀態已發佈 - 2006 五月 1

指紋

Interpolation
Color
Pixels
Image quality
Costs
Sensors
Experiments

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

引用此文

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abstract = "Demosaicing is a process of obtaining a fullcolor image by interpolating the missing colors of an image captured from a single sensor color filter array. This paper provides an effective and low-complexity iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color-difference interpolation to update red, blue, and green pixels. Based on our experiments of images, we enable the algorithm a well-designed stopping condition and predetermine the proper weights of interpolation. Experimental results show that the proposed method performs much better than three state-of-the-art demosaicing techniques in terms of both computational cost and image quality. In comparison to the algorithm of successive approximation, the algorithm proposed here reduces mean squared error up to 14.5{\%} while requiring computational cost only 22{\%} on average. That is, it takes less time but performs better.",
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