Abstract
Recently, deep convolutional neural networks (CNNs) in single image super-resolution (SISR) have received excellent performance. However, most deep-learning-based methods do not make full use of low-level features extracted from the original low-resolution (LR) image, which may reduce the quality of reconstructed image. To address these issues, we propose a method which can connect the low-level features from almost all convolutional layers. Our method use the interpolated low-resolution image as input, employ many skip-connections to combine low-level image features with the final reconstruction process, these feature fusion strategies are based on pixel-level summation operations. After merging the previous convolution features, residual images are used to directly reconstruct high-resolution (HR) images. Experiments demonstrate that the proposed method is superior to the state-of-the-art methods.
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Acknowledgement
This research was supported partially by the National Natural Science Foundation of China (Nos. 61372130, 61432014, 61871311). The authors would like to thank our tutor, Professor Lu Wen, his valuable remarks and suggestions inspired us a lot.
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Bai, F., Wang, R., Sun, X., Sun, H., Lu, W. (2018). Deep Convolutional Neural Network with Feature Fusion for Image Super-Resolution. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_20
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DOI: https://doi.org/10.1007/978-981-13-2922-7_20
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