文件名称:Linear_and_non
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Linear and non-linear approximation with wavelets
Linear approximation is obtained by keeping M low frequency coefficients and then applying the inverse wavelet transform. Here we take M=n^2/4 by keeping n/2 x n/2 low frequency coefficients.
Linear approximation is obtained by keeping M=n^2/4 best coefficients, that are the M largest amplitude coefficients.
We can now compute the approximation error and display both the coefficients and the resulting images. The non-linear approximation is usually much better for images that contains edges because linear approximation blurs features (it is non-adaptive).
Linear approximation is obtained by keeping M low frequency coefficients and then applying the inverse wavelet transform. Here we take M=n^2/4 by keeping n/2 x n/2 low frequency coefficients.
Linear approximation is obtained by keeping M=n^2/4 best coefficients, that are the M largest amplitude coefficients.
We can now compute the approximation error and display both the coefficients and the resulting images. The non-linear approximation is usually much better for images that contains edges because linear approximation blurs features (it is non-adaptive).
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