@article {62779, title = {Image reconstruction from undersampled confocal microscopy data using multiresolution based maximum entropy regularization}, journal = {Journal of Instrumentation}, volume = {14}, year = {2019}, month = {07/2019}, abstract = {

We consider the problem of reconstructing 2D images from randomly under-sampled confocal microscopy samples. The well known and widely celebrated total variation regularization, which is the

l1 norm of derivatives, turns out to be unsuitable for this problem; it is unable to handle both noise and under-sampling together. This issue is linked with the notion of phase transition phenomenon observed in compressive sensing research, which is essentially the break-down of total variation methods, when sampling density gets lower than certain threshold. The severity of this breakdown is determined by the so-called mutual incoherence between the derivative operators and measurement operator. In our problem, the mutual incoherence is low, and hence the total variation regularization gives serious artifacts in the presence of noise even when the sampling density is not very low. There has been very few attempts in developing regularization methods that perform better than total variation regularization for this problem. We develop a multi-resolution based regularization method that is adaptive to image structure. In our approach, the desired reconstruction is formulated as a series of coarse-to-fine multi-resolution reconstructions; for reconstruction at each level, the regularization is constructed to be adaptive to the image structure, where the information for adaption is obtained from the reconstruction obtained at coarser resolution level. This adaptation is achieved by using maximum entropy principle, where the required adaptive regularization is determined as the maximizer of entropy subject to the information extracted from the coarse reconstruction as constraints. We also utilize the directionally adaptive second order derivatives for constructing the regularization with directions guided by the given coarse reconstruction, which leads to an improved suppression of artifacts. We demonstrate the superiority of the proposed regularization method over existing ones using several reconstruction examples.

}, doi = {10.1088/1748-0221/14/07/P07015}, author = {Francis, B and Mathew, Manoj and Arigovindan, M} } @article {45428, title = {Multiresolution-based weighted regularization for denoised image interpolation from scattered samples with application to confocal microscopy }, journal = {Journal of the Optical Society of America A}, volume = {35}, year = {2018}, month = {10/2018}, pages = {1749-1759}, abstract = {

The problem of reconstructing an image from nonuniformly spaced, spatial point measurements is frequently encountered in bioimaging and other scientific disciplines. The most successful class of methods in handling this problem uses the regularization approach involving the minimization of a derivative-based roughness functional. It has been well demonstrated, in the presence of noise, that nonquadratic roughness functionals such as l1 measure yield better performance compared to the quadratic ones in inverse problems in general and in deconvolution in particular. However, for the present problem, all well-evaluated methods use quadratic roughness measures; indeed, l1 performs worse than the quadratic roughness when the sampling density is low. This is due to the fact that the mutual incoherence between the measurement operator (dirac-delta) and the regularization operator (derivative) is low in the present problem. Here we develop a new multiresolution-based roughness functional that performs better than l1 and quadratic functionals under a wide range of sampling densities. We also propose an efficient iterative method for minimizing the resulting cost function. We demonstrate the superiority of the proposed regularization functional in the context of reconstructing full images from nonuniformly undersampled data obtained from a confocal microscope.

}, doi = {10.1364/JOSAA.35.001749}, author = {Francis, B and Mathew, Manoj and Arigovindan, M.} } @article {21472, title = {Direct transfer of classical non-separable states into hybrid entangled two photon states.}, journal = {Sci Rep}, volume = {7}, year = {2017}, month = {2017 Aug 04}, pages = {7331}, abstract = {

Hybrid entangled states, having entanglement between different degrees-of-freedom (DoF) of a particle pair, are of great interest for quantum information science and communication protocols. Among different DoFs, the hybrid entangled states encoded with polarization and orbital angular momentum (OAM) allow the generation of qubit-qudit entangled states, macroscopic entanglement with very high quanta of OAM and improvement in angular resolution in remote sensing. Till date, such hybrid entangled states are generated by using a high-fidelity polarization entangled states and subsequent imprinting of chosen amount of OAM using suitable mode converters such as spatial light modulator in complicated experimental schemes. Given that the entangled sources have feeble number of photons, loss of photons during imprinting of OAM using diffractive optical elements limits the use of such hybrid states for practical applications. Here we report, on a simple generic experimental scheme to generate hybrid entangled states in polarization and OAM through direct transfer of classical non-separable states of the pump beam in parametric down conversion process. As a proof of principle, using local non-separable pump states of OAM mode l = 3, we have produced quantum hybrid entangled states with entanglement witness parameter of ~1.25 {\textpm} 0.03 violating by 8 standard deviation.

}, issn = {2045-2322}, doi = {10.1038/s41598-017-07318-1}, author = {Jabir, M V and Apurv Chaitanya, N and Mathew, Manoj and Samanta, G K} } @article {21471, title = {Hierarchies in light sensing and dynamic interactions between ocular and extraocular sensory networks in a flatworm.}, journal = {Sci Adv}, volume = {3}, year = {2017}, month = {2017 Jul}, pages = {e1603025}, abstract = {

Light sensing has independently evolved multiple times under diverse selective pressures but has been examined only in a handful among the millions of light-responsive organisms. Unsurprisingly, mechanistic insights into how differential light processing can cause distinct behavioral outputs are limited. We show how an organism can achieve complex light processing with a simple "eye" while also having independent but mutually interacting light sensing networks. Although planarian flatworms lack wavelength-specific eye photoreceptors, a 25 nm change in light wavelength is sufficient to completely switch their phototactic behavior. Quantitative photoassays, eye-brain confocal imaging, and RNA interference/knockdown studies reveal that flatworms are able to compare small differences in the amounts of light absorbed at the eyes through a single eye opsin and convert them into binary behavioral outputs. Because planarians can fully regenerate, eye-brain injury-regeneration studies showed that this acute light intensity sensing and processing are layered on simple light detection. Unlike intact worms, partially regenerated animals with eyes can sense light but cannot sense finer gradients. Planarians also show a "reflex-like," eye-independent (extraocular/whole-body) response to low ultraviolet A light, apart from the "processive" eye-brain-mediated (ocular) response. Competition experiments between ocular and extraocular sensory systems reveal dynamic interchanging hierarchies. In intact worms, cerebral ocular response can override the reflex-like extraocular response. However, injury-regeneration again offers a time window wherein both responses coexist, but the dominance of the ocular response is reversed. Overall, we demonstrate acute light intensity-based behavioral switching and two evolutionarily distinct but interacting light sensing networks in a regenerating organism.

}, issn = {2375-2548}, doi = {10.1126/sciadv.1603025}, author = {Shettigar, Nishan and Joshi, Asawari and Dalmeida, Rimple and Gopalkrishna, Rohini and Chakravarthy, Anirudh and Patnaik, Siddharth and Mathew, Manoj and Palakodeti, Dasaradhi and Gulyani, Akash} }