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An efficient and lightweight image super-resolution with feature network
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NSTL
Elsevier
Despite the great success of deep learning approaches in image super-resolution tasks in recent years, a large proportion of the approaches emphasize designing complex network structures to continuously deepen the network to achieve superior reconstruction performance. However, some shortcomings need to be addressed, such as the large number of parameters required for training the complex network structure, which makes the model difficult to optimize and train. To address the above problems, we propose an accurate and lightweight neural network which is called the feature supplement network (FSN) to achieve image reconstruction. Specifically, we design a novel residual feature supplement structure(RFSS), which is composed of some residual feature supplement groups (RFSGs) and layer feature attention module (LFAM). Each RFSG contains several heterogeneous residual channel attention blocks (HRCAB) with different levels of skip connections. In RFSS, the stacked RFSG is able to extract features efficiently with limited parameters. Meanwhile, LFAM further improves the feature representation capability of the network because it is capable of capturing more discriminative features from the original image by considering the correlation between the extracted features of different depths. Experiments of various models conducted on benchmark datasets show that our proposed method not only can achieve comparable results compared to the state-of-the-art methods, but also effectively recover high-quality images, even when using limited parameters than the existing method for image super-resolution. The codes of our proposed structure-FSN are accessible on https: //github.com/ReckonerInheritor/RFSN.
NSTL
Elsevier
