273 research outputs found
Sparse And Low Rank Decomposition Based Batch Image Alignment for Speckle Reduction of retinal OCT Images
Optical Coherence Tomography (OCT) is an emerging technique in the field of
biomedical imaging, with applications in ophthalmology, dermatology, coronary
imaging etc. Due to the underlying physics, OCT images usually suffer from a
granular pattern, called speckle noise, which restricts the process of
interpretation. Here, a sparse and low rank decomposition based method is used
for speckle reduction in retinal OCT images. This technique works on input data
that consists of several B-scans of the same location. The next step is the
batch alignment of the images using a sparse and low-rank decomposition based
technique. Finally the denoised image is created by median filtering of the
low-rank component of the processed data. Simultaneous decomposition and
alignment of the images result in better performance in comparison to simple
registration-based methods that are used in the literature for noise reduction
of OCT images.Comment: Accepted for presentation at ISBI'1
Toward Guaranteed Illumination Models for Non-Convex Objects
Illumination variation remains a central challenge in object detection and
recognition. Existing analyses of illumination variation typically pertain to
convex, Lambertian objects, and guarantee quality of approximation in an
average case sense. We show that it is possible to build V(vertex)-description
convex cone models with worst-case performance guarantees, for non-convex
Lambertian objects. Namely, a natural verification test based on the angle to
the constructed cone guarantees to accept any image which is sufficiently
well-approximated by an image of the object under some admissible lighting
condition, and guarantees to reject any image that does not have a sufficiently
good approximation. The cone models are generated by sampling point
illuminations with sufficient density, which follows from a new perturbation
bound for point images in the Lambertian model. As the number of point images
required for guaranteed verification may be large, we introduce a new
formulation for cone preserving dimensionality reduction, which leverages tools
from sparse and low-rank decomposition to reduce the complexity, while
controlling the approximation error with respect to the original cone
Low-rank sparse matrix decomposition for sparsity-driven SAR image reconstruction
We consider the development of a synthetic aperture radar (SAR) image reconstruction method that decomposes the imaged field into a sparse and a low-rank component. Such a decomposition is of interest in image analysis tasks such as segmentation and background subtraction. Conventionally, such
operations are performed after SAR image formation. However image formation methods may produce images that are not well suited for such interpretation tasks since they do not incorporate interpretation objectives to the SAR imaging problem. We exploit recent work on sparse and low-rank decomposition of matrices and incorporate such a decomposition into the process of SAR image formation. The outcome is a method that jointly reconstructs a SAR image and decomposes the formed image into its low-rank background and spatially sparse components. We demonstrate the effectiveness of the proposed method on both synthetic and real SAR images
Deformable face ensemble alignment with robust grouped-L1 anchors
Many methods exist at the moment for deformable face fitting. A drawback to nearly all these approaches is that they are (i) noisy in terms of landmark positions, and (ii) the noise is biased across frames (i.e. the misalignment is toward common directions across all frames). In this paper we propose a grouped -norm anchored method for simultaneously aligning an ensemble of deformable face images stemming from the same subject, given noisy heterogeneous landmark estimates. Impressive alignment performance improvement and refinement is obtained using very weak initialization as "anchors"
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