Learning sparse codes for image reconstruction
نویسندگان
چکیده
We propose a new algorithm for the design of overcomplete dictionaries for sparse coding that generalizes the Sparse Coding Neural Gas (SCNG) algorithm such that it is not bound to a particular approximation method for the coefficients of the dictionary elements. In an application to image reconstruction, a dictionary that has been learned using this algorithm outperforms a dictionary that has been obtained from the widely-used K-SVD algorithm, an overcomplete Haar-wavelet dictionary and an overcomplete discrete cosine transformation (DCT).
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