Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
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With the development of deep learning, single image super-resolution (SISR) has achieved significant breakthroughs. Recently, methods to enhance the performance of SISR networks based on global feature interactions have been proposed. However, the capabilities of neurons that need to adjust their function in response to the context dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN), a hybrid network composed of a Convolutional Neural Network (CNN) and a Transformer. Specifically, a novel Cross-receptive Field Guide Transformer (CFGT) is designed to adaptively modify the network weights by using modulated convolution kernels combined with local representative semantic information. In addition, a CNN-based Cross-scale Information Aggregation Module (CIAM) is proposed to make the model better focused on potentially practical information and improve the efficiency of the Transformer stage. Extensive experiments show that our proposed CFIN is a lightweight and efficient SISR model, which can achieve a good balance between computational cost and model performance.
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