Optimization of Demosaicing Algorithm for Autofluorescence Imaging System
QIN Shijia (秦诗佳), WANG Zhiwu (王志武), YAN Guozheng* (颜国正), KUANG Shuai (邝帅), CHENG Hao (程浩), XIAO Jie (肖杰)
2019, 24 (4):
Autofluorescence imaging (AFI) systems are widely used in the detection of precancerous lesions.
Fluorescence images of precancerous tissue are usually red (R) or blue (B), so this kind of system has high requirement
for colour recovery, especially in R and B channels. Besides, AFI system requires bulk data transmission
with no time delay. Existing colour recovery algorithms focus more on green (G) channel, overlooking R and B
channels. Although the state-of-art demosaicing algorithms can perform well in colour recovery, they often have
high computational cost and high hardware requirements. We propose an efficient interpolation algorithm with
low complexity to solve the problem. When calculating R and B channel values, we innovatively propose the
diagonal direction to select the interpolation direction, and apply colour difference law to make full use of the
correlation between colour channels. The experimental results show that the peak signal-to-noise ratios (PSNRs)
of G, R and B channels reach 37.54, 37.40 and 38.22 dB, respectively, which shows good performance in recovery
of R and B channels. In conclusion, the algorithm proposed in this paper can be used as an alternative to the
existing demosaicing algorithms for AFI system.
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