We introduce a new generation of 3D imaging devices based on quantum plenoptic imaging. Position-momentum entanglement and photon number correlations are exploited to provide a scan-free 3D image after post-processing of the collected light intensity signal. We explore the steps toward designing and implementing quantum plenop- tic cameras with dramatically improved performances, unattainable in standard plenoptic cameras, such as diffraction-limited resolution, large depth of focus, and ultra-low noise. However, to make these new types of devices attractive to end-users, two main challenges need to be tackled: the reduction of the acquisition times, that for the commercially available high-resolution cameras would be from tens of seconds to a few minutes, and a speed-up in processing the large amount of data that are acquired, in order to retrieve 3D reconstructions or refocused 2D images. To address these challenges, we are employing high-resolution SPAD (single photon avalanche diode) arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim of reducing both the acquisition and the elaboration time by one or possibly two orders of magnitude. Moreover, in order to achieve the quantum limit and further increase the volumetric resolution beyond the Rayleigh diffraction limit, we explored dedicated pro- tocols based on quantum Fisher information. Finally, we discuss how this new generation of quantum plenoptic devices could be exploited in different fields of research, such as 3D microscopy and space imaging.
In this paper we propose a method to get fine registration of high resolution multispectral images. The algorithm supposes that a coarse registration, based on ancillary information, has been already performed. It is known, in fact, that residual distortions remain, due to the combined effects of Earth rotation and curvature, view geometry, sensor operation, variations in platform velocity, atmospheric and terrain effects.
The algorithm grounds its main idea on the information-theoretic approach to register volumetric medical images of different modalities. Registration is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized. The idea is that the join information is maximized when the two images are at their best registration. This approach works directly with image data but in principle it can be applied in any transformed domain. While the original algorithm has been thought to make registration in a limited search space (i.e. translation and orientation), in the remote sensing framework the class of transformations is extended allowing scaling, shearing or a general polynomial model. The maximization of the target function is performed using both the stochastic gradient descent algorithm and the simulated annealing, since the former is known to occasionally deadlock in local maxima.
We have applied the algorithm on a SPOT-5 couple of images, achieving the registration of chips of size
256x256 pixels at time. Accuracy has been obtained comparing the results with the outcomes of a commercial software that adopts a sort of Normalized Cross-Correlation method. On 143 chips taken throughout the image, the final translation accuracy resulted well below 1 pixel and the rotation accuracy about 0.015deg.
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