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S-GWO-FH: sparsity-based grey wolf optimization algorithm for face hallucination.
- Source :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications; Sep2022, Vol. 26 Issue 18, p9323-9338, 16p
- Publication Year :
- 2022
-
Abstract
- In recent years, face hallucination (FH) techniques are developed to generate the high-resolution (HR) version of the captured blurry low-resolution face images. The popular FH techniques transform the face hallucination problem as a least square representation (LSR) formulation. The performance of these FH techniques entirely depends on how optimally the LSR problem is minimized. Hence, in this paper, a new FH model using a sparsity-based grey wolf optimization algorithm (named S-GWO-FH) is developed which optimizes the LSR problem more efficiently. The concept of sparsity with GWO helps the proposed FH model in ignoring the dislike training images; consequently, reconstructed images have better personal characteristics. It also helps in minimizing computational overhead as it reduces the population size. Moreover, a domain-specific prior is incorporated to initialize the positions of the grey wolves, which helps the GWO algorithm to converse with the more appropriate solution. Several state-of-the-art nature-inspired optimization algorithms and conventional super-resolution techniques are considered for performance comparison. The experiments results show that the proposed S-GWO-FH model is superior to competitive algorithms in terms of reconstruction capability as well as computation time. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14327643
- Volume :
- 26
- Issue :
- 18
- Database :
- Complementary Index
- Journal :
- Soft Computing - A Fusion of Foundations, Methodologies & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 158564050
- Full Text :
- https://doi.org/10.1007/s00500-022-07250-1