Raw Depth Image Enhancement Using a Neural Network

The term image is often used to denote a data format that records information about a scene’s color. This dissertation object focuses on a similar format for recording distance information about a scene, “depth images”. Depth images have been used extensively in consumer-level applications, such as Apple’s Face ID, based on depth images for face recognition. However, depth images suffer from low precision and high errors, and some post-processing techniques need to be utilized to improve their quality. Deep learning, or neural networks, are frameworks that use a series of hierarchically arranged nonlinear networks to process input data. Although each layer of the network is limited in its capabilities, the learning capacity accumulated by the multilayer network becomes very powerful. This dissertation assembles two different deep learning frameworks to solve two different types of raw image preprocessing problems. The first network is the super-resolution network, a nonlinear interpolation of low-resolution deep images through the deep network to obtain high-resolution images. The second network is the inpainting network, which is used to mitigate the problem of losing specific pixel data in the original depth image for various reasons. This dissertation presents deep images processed by these two frameworks, and the quality of the processed images is significantly improved compared to the original images. The great potential of deep learning techniques in the field of deep image processing is shown

Improved registration for 3D image creation using multiple texel images and incorporating low-cost GPS/INS measurements

The creation of 3D imagery is an important topic in remote sensing. Several methods have been developed to create 3D images from fused ladar and digital images, known as texel images. These methods have the advantage of using both the 3D ladar information and the 2D digital imagery directly, since texel images are fused during data acquisition. A weakness of these methods is that they are dependent on correlating feature points in the digital images. This can be dicult when image perspectives are signicantly dierent, leading to low correlation values between matching feature points. This paper presents a method to improve the quality of 3D images created using existing approaches that register multiple texel images. The proposed method incorporates relatively low accuracy measurements of the position and attitude of the texel camera from a low-cost GPS/INS into the registration process. This information can improve the accuracy and robustness of the registered texel images over methods based on point-cloud merging or image registration alone. In addition, the dependence on feature point correlation is eliminated. Examples illustrate the value of this method for signicant image perspective dierences

Corrosion Stability of Metallic Materials in Dentistry as Studied with Electrochemical Impedance Measurements

The corrosion susceptibility of selected metallic materials frequently employed in prosthetic dentistry has been examined with electrochemical methods. Results have been compared with data derived from breakthrough potential measurements performed with these materials before. Mostly agreement and/or close correlation were found, discrepancies are discussed and tentatively assigned to the different experimental conditions

Magnetic ordering and structural phase transitions in strained ultrathin SrRuO3_{3}/SrTiO3_{3} superlattice

Ruthenium-based perovskite systems are attractive because their Structural, electronic and magnetic properties can be systematically engineered. SrRuO$_3$/SrTiO$_3$ superlattice, with its period consisting of one unit cell each, is very sensitive to strain change. Our first-principles simulations reveal that in the high tensile strain region, it transits from a ferromagnetic (FM) metal to an antiferromagnetic (AFM) insulator with clear tilted octahedra, while in the low strain region, it is a ferromagnetic metal without octahedra tilting. Detailed analyses of three spin-down Ru-t$_{2g}$ orbitals just below the Fermi level reveal that the splitting of these orbitals underlies these dramatic phase transitions, with the rotational force constant of RuO$_6$ octahedron high up to 16 meV/Deg$^2$, 4 times larger than that of TiO$_6$. Differently from nearly all the previous studies, these transitions can be probed optically through the diagonal and off-diagonal dielectric tensor elements. For one percent change in strain, our experimental spin moment change is -0.14$\pm$0.06 $\mu_B$, quantitatively consistent with our theoretical value of -0.1 $\mu_B$.Comment: 3 figures, 1 supplementary material, accepted by Phys. Rev. Let