New family of binary arrays for coded aperture imaging

Gottesman, Stephen R.; Fenimore, E. E.

doi:10.1364/ao.28.004344

Cited by 479 publications

(256 citation statements)

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“…All the aperture patterns we consider in this work (top row) and their calibrated PSFs for two different blur scales (second and bottom row). (a) and (b) aperture masks used in both [13] and [43]; (c) annular mask used in [17]; (d) pattern proposed by [5]; (e) pattern proposed by [4]; (f) and (g) aperture masks used in [15]; (h) MURA pattern used in [10]. Fig.…”

Section: Coded Aperture Selectionmentioning

confidence: 99%

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Single Image Blind Deconvolution with Higher-Order Texture Statistics

Martinello

Favaro

2011

Video Processing and Computational Video

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Abstract. We present a novel method for solving blind deconvolution, i.e., the task of recovering a sharp image given a blurry one. We focus on blurry images obtained from a coded aperture camera, where both the camera and the scene are static, and allow blur to vary across the image domain. As most methods for blind deconvolution, we solve the problem in two steps: First, we estimate the coded blur scale at each pixel; second, we deconvolve the blurry image given the estimated blur. Our approach is to use linear high-order priors for texture and second-order priors for the blur scale map, i.e., constraints involving two pixels at a time. We show that by incorporating the texture priors in a least-squares energy minimization we can transform the initial blind deconvolution task in a simpler optimization problem. One of the striking features of the simplified optimization problem is that the parameters that define the functional can be learned offline directly from natural images via singular value decomposition. We also show a geometrical interpretation of image blurring and explain our method from this viewpoint. In doing so we devise a novel technique to design optimally coded apertures. Finally, our coded blur identification results in computing convolutions, rather than deconvolutions, which are stable operations. We will demonstrate in several experiments that this additional stability allows the method to deal with large blur. We also compare our method to existing algorithms in the literature and show that we achieve state-of-the-art performance with both synthetic and real data.

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Section: Coded Aperture Selectionmentioning

confidence: 99%

“…4. All the patterns have been proposed and used by other researchers [4,5,10,13,15,17]. For each mask and a given blur scale map d, we simulate a coded image by using eq.…”

Section: Performance Comparisonmentioning

confidence: 99%

Single Image Blind Deconvolution with Higher-Order Texture Statistics

Martinello

Favaro

2011

Video Processing and Computational Video

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“…In the initial geometry of CA SM we utilize a MURA pattern [7] of 241x241 element array (dimension 24.1 x 24.1 cm 2 ) placed midway (see Fig. 1) between the tumor central plane and the front surface of the camera by 60cm.…”

Section: Simulated Geometry Using Mcnpxmentioning

confidence: 99%

“…This configuration of MURA geometry is used to cover the field of view. The raw projected (encoded) image is decoded by using correlation with a binary array representing the coded aperture [7]. In the second part of the work, the CA mask was then removed and a LEHR collimator placed in close proximity to the NaI crystal, with a 7cm distance between lesion centre and collimator face.…”

Section: Simulated Geometry Using Mcnpxmentioning

confidence: 99%

“…This means that the collimator tends to be the limiting factor for both system spatial resolution and sensitivity. By contrast, this study focuses on planar SM comparing two different methods: a Low Energy High Resolution (LEHR) collimator and Modified Uniformly Redundant Array (MURA) [7] Coded Aperture (CA) systems in order to explore whether a CA with a standard clinical gamma camera can be used in such an application. The imaging performance of both LEHR collimator and CA imaging were measured by considering two fundamental parameters: lesion contrast and FWHM under a variety of idealized and clinically realistic imaging conditions.…”

Section: Introductionmentioning

confidence: 99%

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