Estudio de técnicas de suavización en imágenes basadas en modelos dispersos
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In the last decade, advanced techniques have been developed for the elimination of noise in the image processing, whose results allow not only a greater denoise, but the preservation of the edges. These techniques include the non-local average method, BDM3D and based on sparse modeling, among others. This last technique has aroused great interest due to the results in which high-intensity noise is eliminated. For them, it uses dictionaries that are built as the filter progresses. This technique has been expanded to be used in combination with neural networks. In this work, the K-SVD smoothing algorithm was implemented. This algorithm is based on sparse modeling. In order to analyze and know the bases, characteristics and results obtained with the dispersed modeling method, it was compared with other noise elimination techniques, based on convolution masks. For this, the response of the filter and its results were analyzed, in terms of noise elimination and edge conservation. For this purpose, natural and synthetic images with different noise levels were used.