A heterogeneous group CNN for image super-resolution

• URL: http://arxiv.org/abs/2209.12406v1
• Date: Mon, 26 Sep 2022 04:14:59 GMT
• Title: A heterogeneous group CNN for image super-resolution
• Authors: Chunwei Tian, Yanning Zhang, Wangmeng Zuo, Chia-Wen Lin, David Zhang, Yixuan Yuan
• Abstract summary: Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. We present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image.
• Score: 127.2132400582117
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR) under complex scenes. In this paper, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent appearance of obtained redundant features, a refinement block with signal enhancements in a serial way is designed to filter useless information. To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel up-sampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://github.com/hellloxiaotian/HGSRCNN.

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