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Enhanced image approximation using shifted rank-1 reconstruction


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  • Low rank approximation has been extensively studied in the past. It is most suitable to reproduce rectangular like structures in the data. In this work we introduce a generalization using "shifted" rank-$ 1 $ matrices to approximate $ \mathit{\boldsymbol{{A}}}\in \mathbb{C}^{M\times N} $. These matrices are of the form $ S_{\mathit{\boldsymbol{{\lambda}}}}(\mathit{\boldsymbol{{u}}}\mathit{\boldsymbol{{v}}}^*) $ where $ \mathit{\boldsymbol{{u}}}\in \mathbb{C}^M $, $ \mathit{\boldsymbol{{v}}}\in \mathbb{C}^N $ and $ \mathit{\boldsymbol{{\lambda}}}\in \mathbb{Z}^N $. The operator $ S_{\mathit{\boldsymbol{{\lambda}}}} $ circularly shifts the $ k $-th column of $ \mathit{\boldsymbol{{u}}}\mathit{\boldsymbol{{v}}}^* $ by $ \lambda_k $.

    These kind of shifts naturally appear in applications, where an object $ \mathit{\boldsymbol{{u}}} $ is observed in $ N $ measurements at different positions indicated by the shift $ \mathit{\boldsymbol{{\lambda}}} $. The vector $ \mathit{\boldsymbol{{v}}} $ gives the observation intensity. This model holds for seismic waves that are recorded at $ N $ sensors at different times $ \mathit{\boldsymbol{{\lambda}}} $. Other examples are a car that moves through a video changing its position $ \mathit{\boldsymbol{{\lambda}}} $ in each of the $ N $ frames, or non-destructive testing based on ultrasonic waves that are reflected by defects inside the material.

    The main difficulty of the above stated problem lies in finding a suitable shift vector $ \mathit{\boldsymbol{{\lambda}}} $. Once the shift is known, a simple singular value decomposition can be applied to reconstruct $ \mathit{\boldsymbol{{u}}} $ and $ \mathit{\boldsymbol{{v}}} $. We propose a greedy method to reconstruct $ \mathit{\boldsymbol{{\lambda}}} $. By using the formulation of the problem in Fourier domain, a shifted rank-$ 1 $ approximation can be calculated in $ O(NM\log M) $. Convergence to a locally optimal solution is guaranteed. Furthermore, we give a heuristic initial guess strategy that shows good results in the numerical experiments.

    We validate our approach in several numerical experiments on different kinds of data. We compare the technique to shift-invariant dictionary learning algorithms. Furthermore, we provide examples from application including object segmentation in non-destructive testing and seismic exploration as well as object tracking in video processing.

    Mathematics Subject Classification: Primary: 65F18, 65T99; Secondary: 86A22.


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  • Figure 1.  Input data: Cartoon-like, natural, ultrasonic and seismic images

    Figure 2.  (a) Singular value ratio to $ \||\hat{\bm{{A}}}|\|_2 $ after the individual steps of Algorithm 3. (b) Average approximation error over all kinds of input data

    Figure 3.  Reconstruction of Lena image using 1, 5 and 10 shifted rank-$ 1 $ matrices

    Figure 4.  Approximation error of all algorithms for different kinds of input data plotted against the storage costs

    Figure 5.  Sparse approximation of image "phantom" using Wavelets (left), SR1 (middle) and UC-DLA (right)

    Figure 6.  (a) Average runtime of data approximation against the number of rows. (b) Average runtime of matrix vector multiplication using different number of shifted rank-$ 1 $ matrices

    Figure 7.  Separation of an ultrasonic image in two signals (top and bottom) using SR1 (left), MoTIF (middle) and UC-DLA (right)

    Figure 8.  Identified earth layer reflection in noisy seismic image

    Figure 9.  Tracked route in original video (top) and reconstructed singular vectors $ \mathit{\boldsymbol{{u}}}^1 $, $ \mathit{\boldsymbol{{u}}}^2 $ (middle); as comparison the reconstructed background and person using MAMR is shown (bottom)

    Figure 10.  First and last frame of the soccer video clip

    Figure 11.  Tracked objects in soccer clip (top): advertising banners, referee and time stamp. As comparison the reconstructed background and foreground using MAMR is shown (bottom)

    Table 1.  Mean number of iterations for different kinds of input data

    data global local total
    orthogonal 139 21 160
    natural 137 24 161
    cartoon 91 15 106
    seismic 95 16 111
    ultrasound 95 18 113
     | Show Table
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