Abstract An improved K-SVD method based on non-noisy pixel reconstruction (PK-SVD) is proposed to filter impulse noise. In the phase of image reconstruction, non-noisy pixels are applied in the construction of optimal function to obtain the reconstructed image and improve the filtering performance, and the optimal function is solved by integrating the hierarchical property into the OMP algorithm. In the phase of dictionary training, PK-SVD uses the iterant K-singular value decomposition to renovate both atoms and their coefficients rather than fixes the coefficients. The simulation results show that compared with the other three methods, PK-SVD obtains the sparsest dictionary and the clearest image with higher peak signal to noise ratio.
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2014, Vol. 27 
