Fast neural network for TV super resolution scaling-up system

In this paper, we propose a modified architecture aimed at reducing the computational demands of the generative adversarial network for super-resolution image generation. To achieve this, we embedded depth-wise and point-wise convolution into the convolution layer, effectively decreasing operational complexity and improving the overall network structure. For training and validation, we utilized a dataset consisting of 900 image pairs with resolutions of 480 x 270 and 1920 x 1080. Our experimental results demonstrated that the proposed method can reduce computational operators by 63% compared to the original network, while still maintaining the quality of super-resolution images. To enable real-time implementation, the architecture with light model subsequently deployed it on a GPU processor, allowing for efficient scaling of TV signals for 16x resolution expansion. Our experiments showed that the peak signal-to-noise ratio (PSNR) reached approximately 28 dB, and the processing rate ranged from 6 to 14 frames per second. The network effectively produced output with 16 times greater resolution without introducing any blurring and obvious artifact.

Automated summary

This paper proposes a modified architecture to reduce the computational demands of the generative adversarial network for super-resolution image generation. The experimental results demonstrate that the proposed method can reduce computational operators by 63% while maintaining the quality of super-resolution images.