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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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publications
Deep Spectral Super-Resolution with Noisy Input
Published in IGARSS, 2019
This paper proposed an unsupervised deep spectral super-resolution method, which employs a DCNN to generate the latent HSI from an input RGB and encourages it to fit the input RGB image through down-sampling in spectral domain as well as a sparse gradient prior in spatial domain.
Recommended citation: Z. Lang, L. Zhang, W. Wei, J. Nie, C. Tian and Y. Zhang, "Deep Spectral Super-Resolution with Noisy Input," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 624-627, doi: 10.1109/IGARSS.2019.8900510. https://ieeexplore.ieee.org/document/8900510
Pixel-Aware Deep Function-Mixture Network for Spectral Super-Resolution
Published in AAAI, 2020
This paperpropose a pixel-aware deep function-mixture network for SSR, which enables us to pixel-wisely determine the receptive field size and the mapping function.
Recommended citation: Zhang L, Lang Z, Wang P, et al. Pixel-aware deep function-mixture network for spectral super-resolution[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 12821-12828. https://ojs.aaai.org/index.php/AAAI/article/view/6978
Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks
Published in TIP, 2021
This paper present an embarrassingly simple but effective binarization scheme for SISR, which can obviously relieve the performance degeneration resulted from network binarization and is applicable to different DCNN architectures. Specifically, we force each weight to follow a compact uniform prior, with which the weight will be given a very small absolute value close to zero and its binarization result can be straightforwardly reversed even by a small backpropagated gradient. By doing this, the flexibility and the generalization performance of the binarized network can be improved.
Recommended citation: L. Zhang, Z. Lang, W. Wei and Y. Zhang, "Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks," in IEEE Transactions on Image Processing, vol. 30, pp. 3934-3945, 2021, doi: 10.1109/TIP.2021.3066906. https://ieeexplore.ieee.org/document/9384273