GOA-ISR: A Grasshopper Optimization Algorithm for Improved Image Super-Resolution
الموضوعات :
Bahar Ghaderi
1
,
Hamid Azad
2
,
Hamed Agahi
3
1 - Department of Electrical-Telecommunication Engineering,Faculty of Engineering Shiraz Branch ,Islamic Azad University,Shiraz,Iran
2 - Department of Electrical-Telecommunication Engineering,Faculty of Engineering Shiraz Branch ,Islamic Azad University,Shiraz,Iran
3 - Department of Electrical-Telecommunication Engineering,Faculty of Engineering Shiraz Branch ,Islamic Azad University,Shiraz,Iran
الکلمات المفتاحية: Super Resolution (SR), High-Resolution (HR), Low-Resolution (LR), Learning-based Methods, Grasshopper Optimization Algorithm (GOA).,
ملخص المقالة :
The image super-resolution (ISR) process provides a high-resolution (HR) image from an input low-resolution (LR) image. This process is an important and challenging issue in computer vision and image processing. Various methods are used for ISR, that learning-based methods are one of the most widely used methods in this field. In this approach, a set of training images is used in various learning based ISR methods to reconstruct the input LR image. To this end, appropriate reconstruction weights for the image must be computed. In general, the least-squares estimation (LSE) approach is used for obtaining optimal reconstruction weights. The accuracy of SR depends on the effectiveness of minimizing the LSE problem. Therefore, it is still a challenge to obtain more accurate reconstruction weights for better SR processing. In this study, a Grasshopper Optimization Algorithm (GOA)-based ISR method (GOA-ISR) is proposed in order to minimize the LSE problem more effectively. A new formulation for the upper bound and the lower bound is introduced to make the search process of the GOA algorithm suitable for ISR. The simulation results on DIVerse 2K (DIV2K) dataset, URBAN100, BSD100, Set 14 and Set 5 datasets affirm the advantage of the proposed GOA-ISR approach in comparison with some other basic Neighbor Embedding (NE), Sparse Coding (SC), Adaptive Sparse, Iterative Kernel Correction (IKC), Second-order Attention Network (SAN), Sparse Neighbor Embedding and Grey Wolf Optimizer (GWO) methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The results of the experiments show the superiority of the proposed method comparing to the best compared method (DWSR) increases 8.613 % PSNR.
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