Effective acquisition, analysis and reconstruction of multi-modal data such as colour and multi-/hyper-spectral imagery is crucial in smart camera applications, where wavelet-based coding and compression of images are highly demanded. Many existing discrete wavelet filtering banks have fixed coefficients hence their performance is highly dependent on the signal/image being processed. To tackle this problem, a unified framework is proposed in this study, which can produce a series of discrete wavelet filtering banks, where many existing discrete wavelet filtering banks become special cases of the framework. For each generated filtering bank, it consists of two decomposition filters and two reconstruction filters through an optimisation process. The efficacy of the filtering banks produced by the framework has been validated in two case studies, including colour image decomposition and reconstruction, and hyperspectral image classification. Comprehensive experiments have demonstrated the superior performance of the proposed framework, which will benefit the efficacy of smart camera and camera network applications.

中文翻译：

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基于通用小波的图像分解与重构框架，用于智能相机应用中的多模式数据分析

有效地获取，分析和重建多模式数据（例如彩色图像和多光谱/高光谱图像）在智能相机应用中至关重要，在智能相机应用中，对基于小波的编码和图像压缩有很高的要求。许多现有的离散小波滤波库具有固定系数，因此其性能高度依赖于要处理的信号/图像。为了解决这个问题，本研究提出了一个统一的框架，该框架可以产生一系列离散小波滤波库，其中许多现有的离散小波滤波库成为该框架的特例。对于每个生成的滤波器组，它通过一个优化过程由两个分解滤波器和两个重构滤波器组成。该框架产生的过滤库的有效性已在两个案例研究中得到验证，包括彩色图像分解和重建，以及高光谱图像分类。全面的实验证明了所提出框架的卓越性能，这将有益于智能相机和相机网络应用的功效。