Super-resolution reconstruction of images based on multi-scale feature aggregation
To address the problems of single extracted feature information and missing image details in the image super-resolution reconstruction process,this paper proposes a new generative adversarial network(DAMFA-GAN)to obtain more realistic and natural reconstructed images.In terms of generator,a Dynamic attention-Multi-scale feature aggregation(DAMFA)incorporating a dynamic attention mechanism is used to obtain multi-scale high-frequency in-formation of each upsampled feature in low-resolution images to improve the quality of the reconstructed images;in terms of discriminator,the ConvTrans Encoder module is designed to enhance the feature information extraction capa-bility to improve the accuracy of discrimination.Experimental results on the Set5,Set14,BSD100 and Urban100 data-sets showed that DAMFA-GAN improved the peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)by an average of 0.50 dB and 0.015 2 respectively compared to SRGAN.At the same time,the high-frequency details and visual effects of super-resolution reconstructed images are also significantly improved.
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