Aiming at the research of real-time data transmission and multi-scale image decomposition of embedded optical sensor array, the principle, method and fusion strategy of multi-sensor image fusion are studied comprehensively, thoroughly and systematically by combining the imaging characteristics of source image with multi-scale geometric analysis tools using machine learning algorithm. A new quality scalable video image coding framework is also proposed in this paper, which is implemented by a multi-scale online dictionary learning algorithm based on structured sparse video signals. For the purpose of different types of images and image fusion, a new high quality scalable video image coding framework based on machine learning algorithm is proposed on the basis of comprehensive analysis of prior information such as imaging mechanism of image sensor and imaging characteristics of source image. A multi-scale online dictionary learning algorithm based on machine learning for sparse video signal structure is proposed. Through the hierarchical structure of wavelet decomposition, the searching domain of online learning is optimized to a hierarchical sparse block, and its sparse representation coefficients are obtained by using machine learning sparse coding idea. The real-time data transmission of embedded optical sensor array based on machine learning and multi-scale image decomposition algorithm proposed in this paper have good fusion performance, which is of great significance for further research and engineering application of image fusion technology.

针对嵌入式光学传感器阵列的实时数据传输和多尺度图像分解的研究，结合源图像的成像特性，全面，深入，系统地研究了多传感器图像融合的原理，方法和融合策略。使用机器学习算法的多尺度几何分析工具。本文还提出了一种新的质量可伸缩视频图像编码框架，该框架由基于结构化稀疏视频信号的多尺度在线词典学习算法实现。为了实现不同类型的图像和图像融合，在对图像传感器成像机理和源图像成像特性等先验信息进行综合分析的基础上，提出了一种基于机器学习算法的高质量可伸缩视频图像编码新框架。提出了一种基于机器学习的稀疏视频信号结构多尺度在线词典学习算法。通过小波分解的分层结构，将在线学习的搜索域优化为分层的稀疏块，并利用机器学习的稀疏编码思想得到其稀疏表示系数。本文提出的基于机器学习和多尺度图像分解算法的嵌入式光学传感器阵列实时数据传输具有良好的融合性能，