In this study we have developed a compact and versatile phase camera functioning as a wavefront sensor for macroscopy or microscopy applications. This device records two intensity images at different focal points and, with the integration of an electrically tunable lens (ETL), operates in real time. Working with intensity images allows achieving high resolutions, near the actual CCD/CMOS sensor resolution. Here we show the application of the camera in two very different scenarios, a macroscopic application, where the camera was coupled with a simple lens relay to study the behavior of a deformable mirror (DM); and characterize defocus and astigmatism in optical lenses. On the second example, the camera was attached directly to a microscope using a simple c-mount to follow human blood moving in real time.
In this study we have designed, assembled, and characterized a wavefront sensor that works with defocused intensity images and the wavefront phase imaging (WFPI) algorithm. This approach allows for the potential utilization of the entire sensor surface, enabling high-resolution operation. This sensor, equipped with an electrically tuneable lens (ETL), performs focus movements of more than 60 Hz, enough for real time applications. We have developed numerical tools, as a practical software environment, with a graphical user interface (GUI), to make the camera a versatile instrument easily adaptable to different experimental setups without drastic changes in the optical configuration. These tools allow to analyse the wavefront in real time to extract the desired metrics and results.
We present a formal inversion of the multiscale discrete Radon trasform, valid both for 2D and 3D. With the transformed data from just one of the four quadrants of the direct 2D Radon transform, or one of the twelve dodecants, in case of 3D Radon transform, we can invert ex- actly and directly, with no iterations, the whole domain. The computational complexity of the proposed algorithms will be O(N log N). With N the total size of the problem, either square or cubic. But this inverse transforms are extremely ill conditioned, so the presence of noise in the transformed domain turns them useless. Still we present both algorithms, and characterize its weakness against noise.
KEYWORDS: Sensors, Radon transform, Digital signal processing, Mobile devices, Detection and tracking algorithms, Image processing, Image segmentation, System on a chip, Lanthanum, Hough transforms
We propose a local bar-shaped structure detector that works in real time on high-resolution images.
It is based on the Radon transform. Specifically in the muti-scale variant, which is especially fast because it works in integer mathematics and does not use interpolation.
The Radon transform conventionally works on the whole image, and not locally. In this paper we describe how by stopping at the early stages of the Radon transform we are able to locate structures locally.
We also provide an evaluation of the performance of the algorithm running on the CPU, GPU and DSP of mobile devices to process at acquisition time the images coming from the device's camera.
Wave Front Phase Imaging (WFPI), a new wafer geometry technique, is presented, that acquires 7.65 million data points in 5 seconds on a full 300mm wafer providing lateral resolution of 96µm. The system has high repeatability with root-mean-square (RMS) standard deviation (σRMS) in the single digit nm for the global wafer geometry and in the sub ångström (Å = 10-10 m) range for the full-wafer nanotopography for both 200mm and 300mm blank silicon wafer. WFPI can collect data on the entire wafer to within a single pixel, in our case 96µm, away from the wafer edge roll off. The flatness of the silicon wafers used to manufacture integrated circuits (IC) is controlled to tight tolerances to help ensure that the full wafer is sufficiently flat for lithographic processing. Advanced lithographic patterning processes require a detailed map of the wafer shape to avoid overlay errors caused by depth-of-focus issues. We present WFPI as a new technique with high resolution and high data count acquired at very high speed.
Wave Front Phase Imaging (WFPI) is used to measure the stria on an artificial, transparent plate made of Schott N-BK7® glass material by accurately measuring the Optical Path Difference (OPD) map. WFPI is a new technique capable of reconstructing an accurate high resolution wave front phase map by capturing two intensity images at different propagation distances. An incoherent light source generated by a light emitting diode (LED) is collimated and transmitted through the sample. The resultant light beam carries the wave front information regarding the refraction index changes inside the sample1. Using this information, WFPI solves the Transport Intensity Equation (TIE) to obtain the wave front phase map. Topography of reflective surfaces can also be studied with a different arrangement where the collimated light beam is reflected and carrying the wave front phase, which again is proportional to the surface topography. Three Schott N-BK7® glass block samples were measured, each marked in which location the wave front phase measurement will be performed2. Although WFPI output is an OPD map, knowing the value of refractive index of the material at the wavelength used in the measurements will lead to also knowing the thickness variations of the plate.
The flatness of the silicon wafers used to manufacture integrated circuits (IC) is controlled to tight tolerances to help ensure that the full wafer is sufficiently flat for lithographic processing. Advanced lithographic patterning processes require a detailed map of the wafer shape to avoid overlay errors caused by depth-of-focus issues. A large variety of new materials are being introduced in Back-End of Lines (BEOL) to ensure innovative architecture for new applications. The standard in-line control plan for the BEOL layer deposition steps is based on film thickness and global stress measurements which can be performed on blanket wafers to check the process equipment performance. However, the challenge remains to ensure high performance metrology control for process equipment during high volume manufacturing. With the product tolerance getting tighter and tighter and architecture more and more complex, there is an increasing demand for knowledge of the wafer shape. In this paper we present Wave Front Phase Imaging (WFPI), a new wafer geometry technique, where 7.65 million data points were acquired in 5 seconds on a full 300mm wafer enabling a lateral resolution of 96μm.
Radon transform, and more specifically the multiscale Discrete Radon Transform, is a valuable tool to find straight structures on images.
But the straight lines that DRT finds are those traversing completely the image, and hence DRT is unable to detect non-straight structures.
That limitation is normally adressed by evaluating Radon transform on non-overlapped subsquares of the image. In this work we will show that the initial partial transform stages of multiscale DRT contain enough information to detect non-straight edges.
By stopping on initial stages of an already linearithmic transform that can be executed on integer arithmethics, the proposed method is found to be much faster than its alternatives.
We will show its ability to process images at video acquisition rate on mobile phones.
Points of view generation allows to create virtual views between two or more cameras observing a scene. This field receives attention from multimedia markets, because sufficiently realistic points of view generation should allow to navigate freely between otherwise fixed points of observation. The new views must be interpolated between sampled data, aided by geometrical information relating real cameras poses, objects in the scene and desired point of view. Normally there are several steps involved, globally known as Structure from Motion (SfM) method. Our study focuses on the last stage; image interpolation based on the disparities between known cameras. In this paper, a new method is proposed that uses depth maps generated by a single camera, named SEBI, allowing a more efficient filling in presence of occlusions. Occlusions are considered during interpolation, creating an occlusion-map and an uncertainty-map using the depth information that SEBI cameras provide.
We present our latest advances in the design and implementation of a tunable automultiscopic display based on the tensor display model. A design comprising a three-layer display was introduced. In such design, front and rear layers were enabled to be controlled in a six-degree of freedom manner related to the central layer of the system. A calibration method consisting on displaying a checkerboard pattern in each layer was proposed. By computing the homography of these patterns with respect to the reference plane, it was possible to estimate the needed adjustments. An implementation based on such design was carried over and calibrated following the aforementioned technique. The obtained results demonstrated the feasibility of such implementation.
Discrete Radon transform is a technique that allows to detect lines in images. It is much lighter to compute than Radon transforms based on Fourier slice theorem that use FFT as basis computing block. Even then, it is not that prone to optimal fine grain parallelization due to the need of running 4 passes to mirrored and flipped versions of the input in order to compute the 4 quadrants comprising 45 degrees each that arises of the decomposition of discrete lines in slope-intercept form. A new method is proposed that can solve the 4 quadrants simultaneously allowing for a more efficient parallelization. In higher dimensions Radon transform needs even more ‘runs’ of the basic algorithm, v.g., in 3 dimensions instead of 4 quadrants there are 12 dodecants to be solved. The proposed method can be extended to alleviate also the problem in those higher dimensions achieving an even greater gain.
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