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Two dimensional analysis sparse model | IEEE Conference Publication | IEEE Xplore

Two dimensional analysis sparse model


Abstract:

An analysis sparse model represents an image signal by multiplying it using an analysis dictionary, leading to a sparse outcome. It transforms an image (two dimensional s...Show More

Abstract:

An analysis sparse model represents an image signal by multiplying it using an analysis dictionary, leading to a sparse outcome. It transforms an image (two dimensional signal) into a one-dimensional (1D) vector. However, this 1D model ignores the two dimensional property and breaks the local spatial correlation inside images. In this paper, we propose a two dimensional (2D) analysis sparse model. Our 2D model uses two analysis dictionaries to efficiently exploit the horizontal and vertical features simultaneously. The corresponding sparse coding and dictionary learning algorithm are also presented in this paper. The 2D sparse model is further evaluated for image denoising. Experimental results demonstrate our 2D analysis sparse model outperforms a state-of-the-art 1D analysis model in terms of both denoising ability and memory usage.
Date of Conference: 15-18 September 2013
Date Added to IEEE Xplore: 13 February 2014
Electronic ISBN:978-1-4799-2341-0

ISSN Information:

Conference Location: Melbourne, VIC, Australia

References

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