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基于扩展小波树的彩色自适应压缩成像

Colored Adaptive Compressed Imaging Based on Extended Wavelet Trees

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摘要

基于扩展小波树理论和多任务贝叶斯模型, 提出了彩色图像自适应压缩采样方法。根据扩展小波树结构中父子系数和兄弟系数的关系, 对彩色图像中红、绿、蓝三通道图像分别进行了自适应压缩采样。利用彩色图像三通道间的相关性和多任务贝叶斯模型, 分别处理了采样得到的三通道高频小波系数, 并重构融合得到彩色图像。研究结果表明, 当采样次数为600、采样率为14.6%时, 利用所提方法得到的彩色重构图像的峰值信噪比均大于27 dB, 色差均值最小, 色差值也趋于稳定, 图像色调保持着较好的一致性和稳定性。

Abstract

An adaptive compressed sampling approach for color images is proposed based on extended wavelet tree theory and multitask Bayesian model. Exploiting the relationship of parent-children coefficients and sibling coefficients in extended wavelet trees, the images in the red, green, blue channels of the color images are adaptively compressed. Exploiting the correlation of the three channels of color images and multitask Bayesian model, the sampled high frequency wavelet coefficients of three channels are dealt, respectively, and then the color images are reconstructed and fused. The research results show that when the sampling times are 600 and the sampling rate is 14.6%, the peak signal to noise ratio values of the colored reconstructed images obtained by proposed method are all above 27 dB, while the mean value of color difference is the least, the color difference values tend to be stable, and the color consistency and stability of the images can be kept well.

Newport宣传-MKS新实验室计划
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中图分类号:TP751

DOI:10.3788/lop56.010301

所属栏目:相干光学与统计光学

基金项目:国家自然科学基金(61271332)

收稿日期:2018-08-06

修改稿日期:2018-09-24

网络出版日期:2018-10-10

作者单位    点击查看

骆乐:南京理工大学电子工程与光电技术学院, 江苏 南京 210094
陈钱:南京理工大学电子工程与光电技术学院, 江苏 南京 210094
刘星炯:南京理工大学电子工程与光电技术学院, 江苏 南京 210094
闫奕芸:南京理工大学电子工程与光电技术学院, 江苏 南京 210094
顾国华:南京理工大学电子工程与光电技术学院, 江苏 南京 210094
何伟基:南京理工大学电子工程与光电技术学院, 江苏 南京 210094
王娅:南京理工大学泰州科技学院基础科学部, 江苏 泰州 225300

联系人作者:何伟基(wslla@126.com)

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引用该论文

Luo Le,Chen Qian,Liu Xingjiong,Yan Yiyun,Gu Guohua,He Weiji,Wang Ya. Colored Adaptive Compressed Imaging Based on Extended Wavelet Trees[J]. Laser & Optoelectronics Progress, 2019, 56(1): 010301

骆乐,陈钱,刘星炯,闫奕芸,顾国华,何伟基,王娅. 基于扩展小波树的彩色自适应压缩成像[J]. 激光与光电子学进展, 2019, 56(1): 010301

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