Abstract: In the field of image Super-Resolution reconstruction (SR), traditional SR techniques such as regression-based methods and CNN-based models fail to retain texture details in the reconstructed images. Conversely, Generative Adversarial Networks (GANs) have significantly enhanced the visual quality of image reconstruction through their adversarial training architecture. However, existing GANs still exhibit limitations in capturing local details and efficiently utilizing features. To address these challenges, we have proposed a super-resolution reconstruction method leveraging local texture adversarial and hybrid attention mechanisms. Firstly, a Local Texture Sampling Module (LTSM) is designed to precisely locate small regions with strong texture features within an image, and a local discriminator then performs pixel-by-pixel evaluation on these regions to enhance local texture details. Secondly, a hybrid attention module is integrated into the generator’s residual module to improve feature utilization and representativeness. Finally, we conducted extensive experiments to validate the effectiveness of our method. The results demonstrate that our method surpasses other super-resolution reconstruction methods in terms of PSNR and SSIM on four benchmark datasets. Furthermore, our method visually generates high-resolution images with richer details and more realistic textures.

Na Zhang, Hanhao Yao, Qingqi Zhang, Xiaoan Bao, Biao Wu and Xiaomei Tu, “Incorporating Local Texture Adversarial Branch and Hybrid Attention for Image Super-Resolution” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151117

@article{Zhang2024,
title = {Incorporating Local Texture Adversarial Branch and Hybrid Attention for Image Super-Resolution},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151117},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151117},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Na Zhang and Hanhao Yao and Qingqi Zhang and Xiaoan Bao and Biao Wu and Xiaomei Tu}
}