实验结果表明了这两种稀疏信号表示理论在图像增强方面具有良好的性能和广阔的应用前景。
The experimental results demonstrate the effectiveness of the spare signal expansion in the applications of image enhancement.
在稀疏信号表示的并行选取字典算法中，当频率不在栅格点上时，对应的幅度估计可能会有很大的偏差。
In parallel basis selection algorithms of sparse signal representation, there will be serious bias in amplitude estimation when frequency is not in grid.
信号的稀疏表示或最佳n -项逼近是数据压缩、噪声抑制等众多应用中的一个重要问题。
Signal sparse representation or the optimal N-term approximation is one of the important problems, which is applied to many areas such as the data compression, denoising.
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