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.
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。
For the advantage of wavelet transform in denoising and data compression, we choose wavelet transform to denoise and compress the data of Near-infrared spectra.
并根据小波变换在噪声滤除及数据压缩方面的优势，选取小波变换对光谱数据进行滤噪和初步压缩。
Aim The data adaptive scalar decomposition transform (SDT) and its application in data compression and denoising are studied.
目的研究常数分解变换法(SDT)及其在数据压缩和消噪上的应用。
Comparing with the adaptive denoising algorithm based on compression ratio and SVD, it avoids calculating the function of image compression ratio and its knee point.
该算法的特点是将能量最小法则和奇异值分解结合起来，在代数空间中建立了一种自适应的图像降噪算法。
According to the characteristics of the seismic data, some data processing algorithms based on FFT and wavelet for denoising, filtering and data compression is put forward.
针对地震波信号白勺特点，提出了基于FFT和小波分析白勺包括去噪、滤波和数据压缩白勺数字信号处理算法。
According to the characteristics of the seismic data, some data processing algorithms based on FFT and wavelet for denoising, filtering and data compression is put forward.
针对地震波信号白勺特点，提出了基于FFT和小波分析白勺包括去噪、滤波和数据压缩白勺数字信号处理算法。
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