High-speed phase-stable swept source optical coherence tomography: functional imaging and biomedical applications

In the past decades, the performance of swept source optical coherence tomography (SS-OCT) has experienced an unprecedented improvement which is mainly driven by the rapidly evolving laser technologies: the state-of-art SS-OCT is now tens of dB more sensitive, six orders of magnitude faster, and seeing ten times deeper than the original version of time domain OCT. Regardless of the abovementioned progress, the phase instability is always considered the biggest weakness of SS-OCT and the mainstream belief often states that the mechanical tuning mechanism of the swept source is to blame. In my study, I first developed a high-speed phase-stable SS-OCT based on a new-generation akinetic laser source, which is electrically tuned in wavelength, in the hope of reducing the phase noise to a shot-noise limited level. The experimental results turned out to be contradicted to the conventional phase noise theory, which inspires my discovery of a completely new interpretation for the phase noise in SS-OCT: I proposed that the timing jitter and scanning variability has to be taken into the consideration in the noise model as multiplicative noises. The theory was later validated by another SS-OCT using a different light source. This study for the first time articulated the phase noise’s origin and composition in the SS-OCT. Although the SS-OCT performs relatively worse in phase stability compared with its spectral-domain counterpart (SD-OCT), it is still valuable since it images at a much faster rate than SD-OCT. Therefore, a better temporal resolution could be achieved, which is particularly attractive in areas such as time lapse imaging. I therefore utilize the system along with other two systems to conduct ex vivo imaging on human tracheobronchial epithelium. It is shown that the SS-OCT system could achieve equally good performance in this task. Moreover, thanks to the higher temporal and temporal frequency resolution, finer structure within the frequency response of the ciliary motion is picked up by our system. During the study of ex vivo ciliary imaging, one of the challenges I was confronted with was the enormous amount of data generated by the SS-OCT, especially when high temporal frequency resolution is required. We thus came up with an idea of applying the compressive sensing (CS) to reduce the data size. Currently, we have demonstrated some preliminary results with using CS on reference k-clock channel compression. In the future, we will apply the same theory to compress the sample channel data, especially or time lapse OCT imaging

Interferometric synthetic aperture sonar system supported by satellite

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Low-discrepancy point sampling of 2D manifolds for visual computing

Point distributions are used to sample surfaces for a wide variety of applications within the fields of graphics and computational geometry, such as point-based graphics, remeshing and area/volume measurement. The quality of such point distributions is important, and quality criteria are often application dependent. Common quality criteria include visual appearance, an even distribution whilst avoiding aliasing and other artifacts, and minimisation of the number of points required to accurately sample a surface. Previous work suggests that discrepancy measures the uniformity of a point distribution and hence a point distribution of minimal discrepancy is expected to be of high quality. We investigate discrepancy as a measure of sampling quality, and present a novel approach for generating low-discrepancy point distributions on parameterised surfaces. Our approach uses the idea of converting the 2D sampling problem into a ID problem by adaptively mapping a space-filling curve onto the surface. A ID sequence is then generated and used to sample the surface along the curve. The sampling process takes into account the parametric mapping, employing a corrective approach similar to histogram equalisation, to ensure that it gives a 2D low-discrepancy point distribution on the surface. The local sampling density can be controlled by a user-defined density function, e.g. to preserve local features, or to achieve desired data reduction rates. Experiments show that our approach efficiently generates low-discrepancy distributions on arbitrary parametric surfaces, demonstrating nearly as good results as popular low-discrepancy sampling methods designed for particular surfaces like planes and spheres. We develop a generalised notion of the standard discrepancy measure, which considers a broader set of sample shapes used to compute the discrepancy. In this more thorough testing, our sampling approach produces results superior to popular distributions. We also demonstrate that the point distributions produced by our approach closely adhere to the blue noise criterion, compared to the popular low-discrepancy methods tested, which show high levels of structure, undesirable for visual representation. Furthermore, we present novel sampling algorithms to generate low-discrepancy distributions on triangle meshes. To sample the mesh, it is cut into a disc topology, and a parameterisation is generated. Our sampling algorithm can then be used to sample the parameterised mesh, using robust methods for computing discrete differential properties of the surface. After these pre-processing steps, the sampling density can be adjusted in real-time. Experiments also show that our sampling approach can accurately resample existing meshes with low discrepancy, demonstrating error rates when reducing the mesh complexity as good as the best results in the literature. We present three applications of our mesh sampling algorithm. We first describe a point- based graphics sampling approach, which includes a global hole-filling algorithm. We investigate the coverage of sample discs for this approach, demonstrating results superior to random sampling and a popular low-discrepancy method. Moreover, we develop levels of detail and view dependent rendering approaches, providing very fine-grained density control with distance and angle, and silhouette enhancement. We further discuss a triangle- based remeshing technique, producing high quality, topologically unaltered meshes. Finally, we describe a complete framework for sampling and painting engineering prototype models. This approach provides density control according to surface texture, and gives full dithering control of the point sample distribution. Results exhibit high quality point distributions for painting that are invariant to surface orientation or complexity. The main contributions of this thesis are novel algorithms to generate high-quality density- controlled point distributions on parametric surfaces and triangular meshes. Qualitative assessment and discrepancy measures and blue noise criteria show their high sampling quality in general. We introduce generalised discrepancy measures which indicate that the sampling quality of our approach is superior to other low-discrepancy sampling techniques. Moreover, we present novel approaches towards remeshing, point-based rendering and robotic painting of prototypes by adapting our sampling algorithms and demonstrate the overall good quality of the results for these specific applications