19. Y Nx3L : unknown reflectivity subject to Elevation profile reconstruction We look for a matrix with the least number of non-zero rows that matches the measurements
20. Mixed-norm minimization subject to Number of columns in Y (window size + polarizations) Probability of recovery failure (Eldar and Rauhut, 2010)
In our model, we try to approximate the ground and canopy components by a summation of sparse profiles. This is quite different to conventional methods which consider each term of the summation to be non-sparse. Okay, so, during this presentation we’ll be concerned with trying to find these terms. The question is: where do we get these terms from?
Here we can compare the CS approach for single signals and for multiple signals. At first glance, they look the same, but we can notice that the bottom image is definitely sharper / has more quality. In addition, if we do a close-up, we can see the noise has been reduced.
Here we can compare the CS approach for single signals and for multiple signals. At first glance, they look the same, but we can notice that the bottom image is definitely sharper / has more quality. In addition, if we do a close-up, we can see the noise has been reduced.
Here we can compare the CS approach for single signals and for multiple signals. At first glance, they look the same, but we can notice that the bottom image is definitely sharper / has more quality. In addition, if we do a close-up, we can see the noise has been reduced.
Here we can compare the CS approach for single signals and for multiple signals. At first glance, they look the same, but we can notice that the bottom image is definitely sharper / has more quality. In addition, if we do a close-up, we can see the noise has been reduced.
Here we can compare the CS approach for single signals and for multiple signals. At first glance, they look the same, but we can notice that the bottom image is definitely sharper / has more quality. In addition, if we do a close-up, we can see the noise has been reduced.
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