Missing texture reconstruction via power spectrum-based sparse representation

This paper presents a method for missing texture reconstruction via power spectrum-based sparse representation. We reconstruct missing areas based on minimizing the mean square error between power spectra (P-MSE). In our method, missing areas are reconstructed by embedding some known patches. Mathematically, we obtain the optimal linear combination of measurement patches by P-MSE minimization. The optimization can be solved as a combinatorial problem based on sparse representaion. In this way, the optimal approximation which minimizes the P-MSE is obtained and we embed it in the missing area. Experimental results show effectiveness of our method for reconstructing texture images.