In this letter, a method based on block-matching and Low-rank matrix recovery (BM-LRMR) is proposed for single image reconstruction. While existing approaches usually use many image sequences to restructure new images, our method tries to use a single image to realize the image reconstruction. In our algorithm, the single image is firstly partitioned into blocks. Then a block-matching technique is employed in grouping and constructing the approximately low-rank matrix. A low-rank matrix recovery is used in low-rank matrix decomposition. Finally, a weighted average strategy is using to compute a final estimate of the image by aggregating all of the obtained local estimates. Experimental results on different applications demonstrate the proposed model can work excellently for single image reconstruction.
Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence