Shuai Cao1,3, Jianan Liang1,2,*, Yongjun Cao1,2,3,4, Jinglun Huang1,4, Zhishu Yang1,4
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1491-1509, 2024, DOI:10.32604/cmc.2024.049314
- 15 October 2024
Abstract The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution (SISR) research. However, the high computational demands of most SR techniques hinder their applicability to edge devices, despite their satisfactory reconstruction performance. These methods commonly use standard convolutions, which increase the convolutional operation cost of the model. In this paper, a lightweight Partial Separation and Multiscale Fusion Network (PSMFNet) is proposed to alleviate this problem. Specifically, this paper introduces partial convolution (PConv), which reduces the redundant convolution operations throughout the model by separating some of the features of… More >
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