Image reconstruction from a Manhattan grid via piecewise plane fitting and Gaussian Markov random fields

2014 IEEE International Conference on Image Processing (ICIP)

2014

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Cutset sampling is a new approach to image sampling where 2D data is recorded densely along intersecting lines. A special case of cutset sampling is Manhattan sampling, where data is sampled densely along evenly-spaced rows and columns. This paper presents a new method for interpolating pixels from their Manhattan samples called the orthogonal gradient (OG) algorithm, which exploits the fact that pixels tend to be correlated along the direction orthogonal to the image gradient. Such an approach is enhanced by Manhattan sampling, where dense sampling along straight lines allows for better reconstruction of both sharp and soft image edges. In particular, the OG algorithm alternates between solving a constrained optimization problem, and changing the weights of the optimization problem according to the direction of the gradient of each new image estimate. The proposed method improves upon previous Manhattan interpolation algorithms, both qualitatively as well as in mean-squared error. Furthermore, unlike the previous algorithms, the OG algorithm can easily be extended to any sampling scheme.

Section: Constrained Optimization Problem Formulation and Solutionmentioning

confidence: 56%

“…Third, the complete segmentation was used to modify the graph underlying a Gaussian Markov Random Field (MRF); the unknown samples were then estimated using a linear MMSE estimator. These results were improved upon in [8], which also operated in three steps. The underlying image model was that of a piecewise-planar image plus noise.…”

Section: Introductionmentioning

confidence: 93%

Image interpolation from Manhattan cutset samples via orthogonal gradient method

2014 IEEE International Conference on Image Processing (ICIP)

2014

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“…The authors proposed using a Manhattan grid sensor layout where sensors are placed along evenly-spaced rows and columns, as shown in Figure 1(e), and showed that in the context of specific decentralized estimation and communication strategies, this permitted a tradeoff in which communication energy could be substantially reduced with only modest decreases in performance. Previously, the Manhattan grid topology was also used in image processing applications, such as 2D bilevel lossy and lossless image coding [9][10][11] and grayscale image reconstruction [12,13]. A sampling theorem for Manhattan grids has also been derived [14].…”

Section: Introductionmentioning

confidence: 98%

Performance-energy tradeoffs in cutset wireless sensor networks

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

2014

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This work explores performance vs. communication energy tradeoffs in wireless sensor networks that use the recently proposed cutset deployment strategy in which sensors are placed densely along a grid of intersecting lines. For a given number of sensors, intersensor spacing is less for a cutset network than for a conventional lattice deployment, so that cutset networks require less communication energy, albeit with some potential loss in network performance. Previous work analyzed the energy-performance tradeoffs for square-grid cutset networks in the context of specific decentralized algorithms for source localization based on received signal strength (RSS). The current work also considers the RSS based source localization problem. However, it takes a more fundamental approach to analyzing the tradeoff by considering a centralized task, minimum energy communication paths, Maximum Likelihood estimation algorithms and Cramér-Rao bounds. Moreover, it analyzes triangular and honeycomb cutset deployments, in addition to square-grid ones. The results indicate that cutset networks offer sizable decreases in energy with only modest losses of performance.Index Terms-Wireless sensor networks, source localization.

“…As we will argue, this Manhattan grid sensor layout has the advantage that communication requires less power than a randomly distributed network or uniform lattice, thereby increasing battery life. Previously, Manhattan grid sampling has been used in bilevel image coding and reconstruction [6,7,8], as well as grayscale image reconstruction [9,10]. A sampling theorem for Manhattan grids has also been derived [11].…”

Section: Introductionmentioning

confidence: 98%

Energy efficient source localization on a Manhattan grid wireless sensor network

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

2013

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For wireless sensor networks, many decentralized algorithms have been developed to address the problem of locating a source that emits acoustic or electromagnetic waves based on received signal strength. Among the motivations for decentralized algorithms is that they reduce the number of transmissions between sensors, thereby increasing sensor battery life. Whereas most such algorithms are designed for arbitrary sensor placements, such as random placements, this paper focuses on applications that permit a choice of sensor placement. In particular, to make communications costs small, it is proposed to place sensors uniformly along evenly spaced rows and columns, i.e., a Manhattan grid. For such a placement, the Midpoint Algorithm is proposed, which is a simple noniterative decentralized algorithm. The results of this paper show that Manhattan grid networks offer improved accuracy vs. energy tradeoff over randomly distributed networks. Results also show the proposed Midpoint Algorithm offers further energy savings over the recent POCS algorithm.Index Terms-Wireless sensor networks.