School of Science, Northwestern Polytechnical University, Xi’an, 710072, China

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Abstract—Image sparse representation plays a vital role in the process of image reconstruction. In recent years, several pioneering works suggested that signals/images could be represented sparsely with a redundant dictionary. The selection of components from the dictionary directly influences the precision of the reconstructed image, while the scale of dictionary influences the computational efficiency. This paper presents a novel method for image reconstruction, which decomposites the image by concatenating a redundant dictionary of several bases and then reconstruct the image efficiently by means of Matching Pursuit algorithm. The proposed method constructs the concatenated dictionary with cosine bases, wavelet bases and contourlet bases, which will lead to a better approximation of the original image. The experimental results show that the proposed algorithm can greatly reduce the computational complexity and generate a better reconstruction effect compared with previous methods.

Index Terms—Component, concatenated dictionary, sparse decomposition, matching pursuit, orthogonal bases.

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Cite: Zhe Liu, Xiaoxian Zhen, Cong Ma, "A Novel Image Reconstruction Algorithm Based on Concatenated Dictionary," Journal of Computers vol. 7, no. 2, pp. 444-449, 2012.