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- W1883620612 abstract "'Sparse coding' is a ubiquitous strategy employed in the sensory information process system of mammals. Such strategy aims to find a representation of data in which the components of the representation are only rarely significantly active. This paper presents a multiple transform domain image model and demonstrates that it may be used to separate natural images into different ingredients based on the sparse coding strategy. In such model an overcomplete dictionary is constructed by combining different type of complete or over-complete systems that can respectively deal with different image ingredients. Based on a sparse prior restriction, decomposition coefficients are inferred by maximizing a posterior distribution. The resulting coefficients belonging to different systems correspond to different image ingredients. The proposed multiple transform domain image model provides a flexible framework for image ingredient separation which allows one to extract image structure of special interest." @default.
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- W1883620612 date "2007-08-29" @default.
- W1883620612 modified "2023-10-16" @default.
- W1883620612 title "Ingredient Separation of Natural Images: A Multiple Transform Domain Method Based on Sparse Coding Strategy" @default.
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- W1883620612 doi "https://doi.org/10.1007/978-3-540-74260-9_67" @default.
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