Open AccessMulti-focus Source Images Reconstruction based on Adaptive Regional Data Hiding
Author(s) -
Meng-yao Liu,
Quan Zhou,
Yi Zhang,
Yunlong Hu,
Juan-ni Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2005/1/012051
Subject(s) - computer science , computer vision , information hiding , artificial intelligence , focus (optics) , human visual system model , iterative reconstruction , grayscale , image fusion , entropy (arrow of time) , process (computing) , image (mathematics) , physics , quantum mechanics , optics , operating system
Preserving the information of multi-focus source images was neglected in previous image fusion schemes since source image data discarding will happen during the fusion process. Data hiding technology can utilize the redundant bits to embed essential secret data. Inspired from that we proposed a multi-focus image reconstruction algorithm using adaptive regional data hiding. Integrating the Human Visual System (HVS) into the reconstruction module, we further proposed a visual grayscale information entropy operator, which is implemented to segment fused images into texture and flat regions for adaptive data hiding after unfocused region data compression. Our method achieves excellent performances in reconstruction Peak signal-to-noise Ratio (PSNR) above 43dB and maintains the satisfying visual effect of the fused images.