Name
Abstract | ||
|---|---|---|
This passage puts forward a Gaussian scale patch group sparse representation method, to solve the shortage problem of traditional image super-resolution restoration schemes. Our image reconstruction method is focused on the optimisation of sparse representation method model, which brings the method and performance improvement to image sparse reconstruction. The overall framework of our approach is as follows. First of all, we utilised the nonlocal similar patches to extract the patch groups, and then using the simultaneous sparse coding to develop a nonlocal extension of Gaussian scale mixture model. In the end, we integrate the patch group model and Gaussian scale sparsity model into encoding framework. The experimental simulation results show that the proposed framework method can both maintain the clarity of the edge and also inhibit the bad artefacts. Our method often provides a higher subjective/objective quality of reconstructed images than other competing methods. |
Year | Venue | Field |
|---|---|---|
2018 | IJIIDS | Iterative reconstruction,Data mining,Pattern recognition,Computer science,Neural coding,Sparse approximation,Gaussian,Artificial intelligence,Gaussian scale mixtures,Superresolution,Encoding (memory),Performance improvement |
DocType | Volume | Issue |
Journal | 11 | 2/3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||