Abstract
The super resolution of a very low resolution face image is a challenge task in computer vision, because it is difficult to learn a non-linear mapping of input-to-target space by deep neural network in one step upsampling. In this paper, we propose an asymptotic Residual Back-Projection Network (RBPNet) to gradually learn residual between the reconstructed face image and the ground truth by self-supervision mechanism. We map the reconstructed high-resolution feature map back to the original low-resolution feature space, use the original low-resolution feature map as a reference to self-supervising the learning of the various layers. The real high-resolution feature maps are approached gradually by iterative residual learning. Meanwhile, we explicitly reconstruct the edge map of face image and embed it into the reconstruction of high-resolution face image to reduce distortion of super-resolution results. Extensive experiments demonstrate the effectiveness and advantages of our proposed RBPNet qualitatively and quantitatively.
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Wang, X., Lu, Y., Chen, X., Li, W., Wang, Z. (2019). RBPNET: An Asymptotic Residual Back-Projection Network for Super Resolution of Very Low Resolution Face Image. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_16
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DOI: https://doi.org/10.1007/978-3-030-36711-4_16
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