109,412 research outputs found
Super-Resolution of Unmanned Airborne Vehicle Images with Maximum Fidelity Stochastic Restoration
Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. One may, then, envision a scenario where a set of LR images is acquired with sensors on a moving platform like unmanned airborne vehicles (UAV). Due to the wind, the UAV may encounter altitude change or rotational effects which can distort the acquired as well as the processed images. Also, the visual quality of the SR image is affected by image acquisition degradations, the available number of the LR images and their relative positions. This dissertation seeks to develop a novel fast stochastic algorithm to reconstruct a single SR image from UAV-captured images in two steps. First, the UAV LR images are aligned using a new hybrid registration algorithm within subpixel accuracy. In the second step, the proposed approach develops a new fast stochastic minimum square constrained Wiener restoration filter for SR reconstruction and restoration using a fully detailed continuous-discrete-continuous (CDC) model. A new parameter that accounts for LR images registration and fusion errors is added to the SR CDC model in addition to a multi-response restoration and reconstruction. Finally, to assess the visual quality of the resultant images, two figures of merit are introduced: information rate and maximum realizable fidelity. Experimental results show that quantitative assessment using the proposed figures coincided with the visual qualitative assessment. We evaluated our filter against other SR techniques and its results were found to be competitive in terms of speed and visual quality
Fast voxel line update for time-space image reconstruction
Recent applications of model-based iterative reconstruction(MBIR) algorithm to time-space Computed Tomography (CT) have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent(ICD) has been found to have relatively low overall computational requirements due to its fast convergence. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. This disadvantage is especially prominent in time-space reconstruction because of the large volume of data. This thesis presents a new data structure, called VL-Buffer , for time-space reconstruction that significantly improves the cache locality while retaining good parallel performance. Experimental results show an average speedup of 40% using VL-Buffer
Novel methods for SAR imaging problems
Ankara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2013.Thesis (Ph. D.) -- Bilkent University, 2013.Includes bibliographical references leaves 62-70.Synthetic Aperture Radar (SAR) provides high resolution images of terrain reflectivity.
SAR systems are indispensable in many remote sensing applications. High
resolution imaging of terrain requires precise position information of the radar
platform on its flight path. In target detection and identification applications,
imaging of sparse reflectivity scenes is a requirement. In this thesis, novel SAR
image reconstruction techniques for sparse target scenes are developed. These
techniques differ from earlier approaches in their ability of simultaneous image
reconstruction and motion compensation. It is shown that if the residual phase
error after INS/GPS corrected platform motion is captured in the signal model,
then the optimal autofocused image formation can be formulated as a sparse
reconstruction problem. In the first proposed technique, Non-Linear Conjugate
Gradient Descent algorithm is used to obtain the optimum reconstruction. To
increase robustness in the reconstruction, Total Variation penalty is introduced
into the cost function of the optimization. To reduce the rate of A/D conversion
and memory requirements, a specific under sampling pattern is introduced. In the
second proposed technique, Expectation Maximization Based Matching Pursuit
(EMMP) algorithm is utilized to obtain the optimum sparse SAR reconstruction.
EMMP algorithm is greedy and computationally less complex resulting in fast
SAR image reconstructions. Based on a variety of metrics, performances of the
proposed techniques are compared. It is observed that the EMMP algorithm has
an additional advantage of reconstructing off-grid targets by perturbing on-grid
basis vectors on a finer grid.Uğur, SalihPh.D
Sparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) produces an image of a sample by
scanning it with a focused beam of electrons. The electrons interact with the
atoms in the sample, which emit secondary electrons that contain information
about the surface topography and composition. The sample is scanned by the
electron beam point by point, until an image of the surface is formed. Since
its invention in 1942, SEMs have become paramount in the discovery and
understanding of the nanometer world, and today it is extensively used for both
research and in industry. In principle, SEMs can achieve resolution better than
one nanometer. However, for many applications, working at sub-nanometer
resolution implies an exceedingly large number of scanning points. For exactly
this reason, the SEM diagnostics of microelectronic chips is performed either
at high resolution (HR) over a small area or at low resolution (LR) while
capturing a larger portion of the chip. Here, we employ sparse coding and
dictionary learning to algorithmically enhance LR SEM images of microelectronic
chips up to the level of the HR images acquired by slow SEM scans, while
considerably reducing the noise. Our methodology consists of two steps: an
offline stage of learning a joint dictionary from a sequence of LR and HR
images of the same region in the chip, followed by a fast-online
super-resolution step where the resolution of a new LR image is enhanced. We
provide several examples with typical chips used in the microelectronics
industry, as well as a statistical study on arbitrary images with
characteristic structural features. Conceptually, our method works well when
the images have similar characteristics. This work demonstrates that employing
sparsity concepts can greatly improve the performance of SEM, thereby
considerably increasing the scanning throughput without compromising on
analysis quality and resolution.Comment: Final publication available at ACS Nano Letter
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
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