A New Time-Effective Weighted Interpolation Method for Color Filter Array Demosaicking
- DOI
- 10.2991/978-94-6463-030-5_93How to use a DOI?
- Keywords
- Color Filter Array (CFA) Demosaicking; Weighted Interpolation; Finite Impulse Response (FIR) Filter
- Abstract
To reduce the cost of storage and size, most digital cameras capture images through a chip whose surface is covered with a color filter array (CFA) where each sensor only samples one of three original color values. To reconstruct a full image, an interpolation process, often called CFA demosaicking, is required to estimate the missing color values. In this paper, a new time-effective weighted interpolation method for CFA demosaicking is proposed. The new algorithm contains three consecutive steps: First, during the interpolation of green plane, the image is divided into three different regions where each of these is handled with different strategies. Second, four-directional weighted interpolation with gradient inverse weighted filtering (GIWF) refinement is utilized in filling the missing red and blue components. Finally, a postprocessing step with 2-dimensional finite impulse response (FIR) filter is used for further enhancement. An analysis of experimental results shows that the proposed algorithm inherits the good traits of edge preservation under lower time cost.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Yuxuan Song PY - 2022 DA - 2022/12/20 TI - A New Time-Effective Weighted Interpolation Method for Color Filter Array Demosaicking BT - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) PB - Atlantis Press SP - 945 EP - 956 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-030-5_93 DO - 10.2991/978-94-6463-030-5_93 ID - Song2022 ER -