Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Orthonormal ridgelets and linear singularities
SIAM Journal on Mathematical Analysis
Bilinear Sparse Coding for Invariant Vision
Neural Computation
Learning Overcomplete Representations
Neural Computation
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
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'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.