Abstract
Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. In this paper, we propose a new multi-scale information distillation network (MSID-N) in the non-subsampled contourlet transform (NSCT) domain for single image super resolution (SISR). MSID-N mainly consists of a series of stacked multi-scale information distillation (MSID) blocks to fully exploit features from images and effectively restore the low resolution (LR) images to high-resolution (HR) images. In addition, most previous methods predict the HR images in the spatial domain, producing over-smoothed outputs while losing texture details. Thus, we integrate NSCT and demonstrate the superiority of NSCT over wavelet transform (WT), and formulate the SISR problem as the prediction of NSCT coefficients, which is able to further make MSID-N preserve richer structure details than that in spatial domain. The experimental results on three standard image datasets show that our proposed method is capable of obtaining higher PSNR/SSIM values and preserving complex edges and curves better than other state-of-the-art methods.
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Acknowledgements
This work was supported in part by the National Science Foundation of China under Grant Nos. 61602226; in part by the PhD Startup Foundation of Liaoning Technical University of China under Grant No. 18-1021; in part by the Basic Research Project of Colleges and Universities of Liaoning Provincial Department of Education under Grant No. LJ2017FBL004.
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Sang, Y., Sun, J., Wang, S., Peng, Y., Zhang, X., Yang, Z. (2019). Multi-scale Information Distillation Network for Image Super Resolution in NSCT Domain. 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_5
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