Semantic Scholar Open Access 2022 134 sitasi

Image Super-resolution with An Enhanced Group Convolutional Neural Network

Chunwei Tian Yixuan Yuan Shichao Zhang Chia-Wen Lin W. Zuo +1 lainnya

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Abstrak

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN.

Topik & Kata Kunci

Computer Science Medicine Engineering

Penulis (6)

Chunwei Tian

Yixuan Yuan

Shichao Zhang

Chia-Wen Lin

W. Zuo

David Zhang

Format Sitasi

APA MLA BibTeX

Tian, C., Yuan, Y., Zhang, S., Lin, C., Zuo, W., Zhang, D. (2022). Image Super-resolution with An Enhanced Group Convolutional Neural Network. https://doi.org/10.48550/arXiv.2205.14548

Akses Cepat

Lihat di Sumber doi.org/10.48550/arXiv.2205.14548

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 134×
Sumber Database: Semantic Scholar
DOI: 10.48550/arXiv.2205.14548
Akses: Open Access ✓