Classical signal representation techniques generally use a description of the components on a basis on which therepresentation of the signal is unique such as wavelets network. Conversely, sparse representations consist in thedecomposition of the signal on a dictionary comprising a number of elements much larger than the dimension of thesignal. This technique can be widely used for representation, compression, denoising and separation of all types ofsignals. Consequently, some researches have confirmed that the use of a predefined dictionary is less efficient than adictionary from training data. So, the idea of this paper is to propose a new technique for the creation of a dictionaryusing the wavelet decomposition to enhance the sparse representation of images. This technique is based on thecombination of sparse coding and the fast wavelet transform algorithms for image representation.Our results obtained using different universal image databases showed greater performances in the representation ofimages when compared to some methods from the state of the art.
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