Abstract
Traditional super-resolution algorithms are computationally intensive and the quality of generated images is not high. In recent years, due to the superiority of GAN in generating image details, it has begun to attract researchers’ attention. This paper proposes a new GAN-based image super-resolution algorithm, which solves the problems of the current GAN-based super-resolution algorithm that the generated image quality is not high and the multi-scale feature information is not processed enough. In the proposed algorithm, a densely connected dilated convolution network module with different dilation rate is added to enhance the network’s ability to generate image features at different scale levels; a channel attention mechanism is introduced in the network to adaptively select the generated image features, which improves the quality of the generated image on the network. After conducting experiments on classic test datasets, the proposed algorithm has improved PSNR and SSIM compared to ESRGAN.
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Su, K., Li, X., Li, X. (2020). Single Image Super-Resolution Based on Generative Adversarial Networks. In: Wang, Y., Li, X., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2020. Communications in Computer and Information Science, vol 1314. Springer, Singapore. https://doi.org/10.1007/978-981-33-6033-4_1
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DOI: https://doi.org/10.1007/978-981-33-6033-4_1
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