期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 23, 期 6, 页码 2569-2582出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2014.2305844
关键词
Dictionary learning; feedforward neural networks; MMSE estimation; nonlinear prediction; single image super-resolution; sparse representations; statistical models; restricted Boltzmann machine; zooming deblurring
资金
- European Research Council under EU
- ERC [320649]
- Intel Collaborative Research Institute for Computational Intelligence
- European Research Council (ERC) [320649] Funding Source: European Research Council (ERC)
We address single image super-resolution using a statistical prediction model based on sparse representations of low-and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low-and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
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