Image Approximation with Orthogonal Bases¶

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This numerical tour uses several orthogonal bases to perform non-linear image approximation.

Best $M$-terms Non-linear Approximation¶

This tours makes use of an orthogonal base $ \Bb = \{ \psi_m \}_{m=0}^{N-1} $ of the space $\RR^N$ of the images with $N$ pixels.

The best $M$-term approximation of $f$ is obtained by a non-linear thresholding

$$ f_M = \sum_{ \abs{\dotp{f}{\psi_m}}>T } \dotp{f}{\psi_m} \psi_m, $$

where the value of $T>0$ should be carefully selected so that only $M$ coefficients are not thresholded, i.e.

$$ \abs{ \enscond{m}{ \abs{\dotp{f}{\psi_m}}>T } } = M. $$

The goal is to use an ortho-basis $ \Bb $ so that the error $ \norm{f-f_M} $ decays as fast as possible when $M$ increases, for a large class of images.

This tour studies several different orthogonal bases: Fourier, wavelets (which is at the heart of JPEG-2000), cosine, local cosine (which is at the heart of JPEG).

First we load an image of $ N = n \times n $ pixels.

Display it.

Fourier Approximation¶

The discrete 2-D Fourier atoms are defined as: $$ \psi_m(x) = \frac{1}{\sqrt{N}} e^{ \frac{2i\pi}{n} ( x_1 m_1 + x_2 m_2 ) }, $$ where $ 0 \leq m_1,m_2 < n $ indexes the frequency.

The set of inner products $ \{ \dotp{f}{\psi_m} \}_m $ is computed in $O(N \log(N))$ operations with the 2-D Fast Fourier Transform (FFT) algorithm (the pylab function is fft2).

Compute the Fourier transform using the FFT algorithm. Note the normalization by $1/\sqrt{N}$ to ensure orthogonality (energy conservation) of the transform.

Display its magnitude (in log scale). We use the pylab function fftshift to put the low frequency in the center.