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In this paper, a 2D CS sparse image reconstruction algorithm based on iterative gradient projection is proposed.
When using a sparse image database, particles could exist at visually unknown places, which introduces difficulties in performing sequential visual place recognition.
First, using the fact that a typical communication footprint is a sparse image, two novel compressed sensing based reconstruction methods are proposed to reduce the number of transmissions required from the sensors compared to a round-robin querying scheme.
Instead of capturing all data points, this technique records data randomly, drawing a sparse image.
Here Word is a 256 × 256 sparse image.
Then, an extension of this study to sparse image representation and lossy coding contexts has been presented in [23].
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Nearby is a small celadon plaque whose sparse images of bamboo and waterbirds seem to have been painted with the tiniest of brushes.
Figure 3 Sparsity: explicite sparse images.
where ∥.∥2 denotes the ℓ2-norm, and f and f ~ are the original and reconstructed sparse images, respectively.
The images at the bottom are not sparse, but their Laplaciens are (images at top). Figure 4 Sparsity: sparse images in a transformed domain (Fourier or wavelet).
It is assumed that the information is concentrated at specific parts of the sparse images like radar spots in [4], or smaller areas in the center of the landscape in [5].
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