In-loop restoration is a post-processing task aiming to reduce losses caused by the quantization and the inverse quantization phases in a video coding process. Emerging in-loop restoration methods, most of them based on deep learning, have reported higher quality gains than classical filters. However, the complexity at the decoder side remains a challenge. The Sparse Restoration Method (SRM) is presented as a low-complexity method that utilizes sparse representation and Natural Scene Statistic metrics to enhance visual quality at the block level. Our method shows potential restoration benefits when applied to synthetic video sequences.