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This paper proposes an automatic parameter selection framework for optimizing the performance of parameter-dependent regularized reconstruction algorithms. The proposed approach exploits a convolutional neural network for direct estimation of the regularization parameters from the acquired imaging data. This method can provide very reliable parameter estimates in a computationally efficient way. The effectiveness of the proposed approach is verified on transform-learning-based magnetic resonance image reconstructions of two different publicly available datasets. This experiment qualitatively and quantitatively measures improvement in image reconstruction quality using the proposed parameter selection strategy versus both existing parameter selection solutions and a fully deep-learning reconstruction with limited training data. Based on the experimental results, the proposed method improves average reconstructed image peak signal-to-noise ratio by a dB or more versus all competing methods in both brain and knee datasets, over a range of subsampling factors and input noise levels. 

In this article we describe a novel enhancement method for images containing filamentous structures. Our method combines a gradient sparsity constraint with a filamentous structure constraint for effective removal of clutter and noise from the background. The method is applied and evaluated on three types of data: confocal microscopy images of neurons, calcium imaging data and images of road pavement. We found that images enhanced by our method preserve both the structure and the intensity details of the original object. In the case of neuron microscopy, we find that the neurons enhanced by our method are better correlated with the original structure intensities than the neurons enhanced by well-known vessel enhancement methods. Experiments on simulated calcium imaging data indicate that both the number of detected neurons and the accuracy of the derived calcium activity improved. Applying our method to real calcium data, more regions exhibiting calcium activity in the full field of view were found. In road pavement crack detection, smaller or milder cracks were detected after using our enhancement method. 

Copper-antimony-sulfide compounds have desirable earth-abundant compositions for application in renewable energy technologies, such as solar energy and waste heat recycling. These compounds can be synthesized by bottom-up, solution-phase techniques that are more energy and time efficient than conventional solid-state methods. Solution-phase methods typically produce nanostructured materials, which adds another dimension to control optical, electrical, and thermal material properties. This study focuses on a modified-polyol, solution-phase synthesis for tetrahedrite (Cu 12 Sb 4 S 13 ), a promising thermoelectric material with potential also for photovoltaic applications. To dope the tetrahedrite and tune material properties, the utility of the modified polyol synthetic approach has been demonstrated as a strategy to produce phase-pure tetrahedrite that incorporates transition metal (Fe, Co, Ni, Zn, Ag) dopants for Cu, Te dopant for Sb, and Se for S. Six of these reported tetrahedrite compounds have not previously been made by solution-phase methods. For the bottom-up formation of the tetrahedrite nanomaterials, the evolution of the chemical phases has been determined by an investigation of the reaction progress as a function of temperature and time. Digenite (Cu 1.8 S), covellite (CuS), and famatinite (Cu 3 SbS 4 ) are identified as key intermediates and are consistently observed for both undoped and doped tetrahedrites. The effect of nanostructuring and doping tetrahedrite on thermal properties has been investigated. It was found that nanostructured undoped tetrahedrite has reduced thermal stability relative to samples made by solid-state methods, while the addition of dopants for Cu increased the thermal stability of the material. Crystallinity, composition, and nanostructure of products and intermediates were characterized by powder X-ray diffraction, scanning electron microscopy with energy dispersive X-ray spectroscopy, and transmission electron microscopy. Thermal properties were investigated by differential scanning calorimetry and thermal gravimetric analysis. This synthetic study with thermal property analysis demonstrates the potential of the modified polyol method to produce tetrahedrite and other copper-antimony-sulfide compounds for thermoelectric and photovoltaic applications.