科研之友
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摘要
Main challenges for image enlargement methods in embedded systems come from the requirements of good performance, low computational cost, and low memory usage. This paper proposes an efficient image enlargement method which can meet these requirements in embedded system. Firstly, to improve the performance of enlargement methods, this method extracts different kind of features for different morphologies with different approaches. Then, various dictionaries based on different kind of features are learned, which represent the image in a more efficient manner. Secondly, to accelerate the enlargement speed and reduce the memory usage, this method divides the atoms of each dictionary into several clusters. For each cluster, separate projection matrix is calculated. This method reformulates the problem as a least squares regression. The high-resolution (HR) images can be reconstructed based on a few projection matrixes. Numerous experiment results show that this method has advantages such as being efficient and real-time and having less memory cost. These advantages make this method easy to implement in mobile embedded system.
