Fast neural network for TV super resolution scaling‐up system

Abstract

AbstractIn 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 × 270 and 1920 × 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 16× 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.