Informative sensing of natural images
Author(s)Chang, Hyun Sung; Weiss, Yair; Freeman, William T.
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The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work has found the story to be more complicated. For example, the projections based on principal component analysis work better than random projections for some images while the reverse is true for other images. Which feature of images makes such a distinction and what is the optimal set of projections for natural images? In this paper, we attempt to answer these questions with a novel formulation of compressed sensing. In particular, we find that bandwise random projections in which more projections are allocated to low spatial frequencies are near-optimal for natural images and demonstrate using experimental results that the bandwise random projections outperform other kinds of projections in image reconstruction.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 16th IEEE International Conference on Image Processing (ICIP 2009)
Institute of Electrical and Electronics Engineers
Hyun Sung Chang, Y. Weiss, and W.T. Freeman. “Informative sensing of natural images.” Image Processing (ICIP), 2009 16th IEEE International Conference on. 2009. 3025-3028. © Copyright 2010 IEEE
Final published version
INSPEC Accession Number: 11151198
uncertain component analysis, natural images, informative sensing, Compressed sensing