In this paper, we provide a sparse image restoration algorithm with a SSIM-based objective function. The proposed technique is a modification to the SSIM-inspired OMP (iOMP) and, and it has two parallel sparse restoration paths. One of them is ${\mathcal{L}}_2$-sense OMP and the other is $\text{SSIM}$-sense OMP (iOMP). Both paths intersects only at the starting point and gives different quality levels after each iteration. This distinction enables us to select the coefficients of the best quality and to avoid the uncertainty issue of iOMP. From the point of view of the SSIM levels, the conducted experiments proved that, the proposed methodology works better than iOMP and OMP. Also, the performance of this method is checked for significance by the t-test, and the obtained results proved that the method works well especially for large images and the data-independent based dictionary.