TY - GEN
T1 - Cutset sampling and reconstruction of images
AU - Farmer, Ashish
AU - Josan, Awlok
AU - Prelee, Matthew A.
AU - Neuhoff, David L.
AU - Pappas, Thrasyvoulos N.
PY - 2011
Y1 - 2011
N2 - This paper presents a new approach to sampling images in which samples are taken on a cutset with respect to a graphical image model. The cutsets considered are Manhattan grids, for example every Nth row and column of the image. Cutset sampling is motivated mainly by applications with physical constraints, e.g. a ship taking water samples along its path, but also by the fact that dense sampling along lines might permit better reconstruction of edges than conventional sampling at the same density. The main challenge in cutset sampling lies in the reconstruction of the unsampled blocks. As a first investigation, this paper uses segmentation followed by linear estimation. First, the ACA method [1] is modified to segment the cutset, followed by a binary Markov random field (MRF) inspired segmentation of the unsampled blocks. Finally, block interiors are estimated from the pixels on their boundaries, as well as their segmentation, with methods that include a generalization of bilinear interpolation and linear MMSE methods based on Gaussian MRF models or separable autocorrelation models. The resulting reconstructions are comparable to those obtained with conventional sampling at higher sampling densities, but not generally as good as conventional sampling at lower rates.
AB - This paper presents a new approach to sampling images in which samples are taken on a cutset with respect to a graphical image model. The cutsets considered are Manhattan grids, for example every Nth row and column of the image. Cutset sampling is motivated mainly by applications with physical constraints, e.g. a ship taking water samples along its path, but also by the fact that dense sampling along lines might permit better reconstruction of edges than conventional sampling at the same density. The main challenge in cutset sampling lies in the reconstruction of the unsampled blocks. As a first investigation, this paper uses segmentation followed by linear estimation. First, the ACA method [1] is modified to segment the cutset, followed by a binary Markov random field (MRF) inspired segmentation of the unsampled blocks. Finally, block interiors are estimated from the pixels on their boundaries, as well as their segmentation, with methods that include a generalization of bilinear interpolation and linear MMSE methods based on Gaussian MRF models or separable autocorrelation models. The resulting reconstructions are comparable to those obtained with conventional sampling at higher sampling densities, but not generally as good as conventional sampling at lower rates.
KW - Markov random fields
KW - cutsets
KW - image reconstruction
KW - interpolation
KW - sampling
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U2 - 10.1109/ICIP.2011.6115843
DO - 10.1109/ICIP.2011.6115843
M3 - Conference contribution
AN - SCOPUS:84856243959
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1909
EP - 1912
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -