A Boosting Method to Face Image Super-resolution
This work addresses face image super-resolution for applications like surveillance or biometrics, but it is incremental as it builds on existing sparsity-based methods.
The paper tackles the problem of face image super-resolution by proposing a weighted-patch method using AdaBoost to prioritize facial patches with richer information, resulting in improved performance over state-of-the-art methods in objective metrics and visual quality.
Recently sparse representation has gained great success in face image super-resolution. The conventional sparsity-based methods enforce sparse coding on face image patches and the representation fidelity is measured by $\ell_{2}$-norm. Such a sparse coding model regularizes all facial patches equally, which however ignores distinct natures of different facial patches for image reconstruction. In this paper, we propose a new weighted-patch super-resolution method based on AdaBoost. Specifically, in each iteration of the AdaBoost operation, each facial patch is weighted automatically according to the performance of the model on it, so as to highlight those patches that are more critical for improving the reconstruction power in next step. In this way, through the AdaBoost training procedure, we can focus more on the patches (face regions) with richer information. Various experimental results on standard face database show that our proposed method outperforms state-of-the-art methods in terms of both objective metrics and visual quality.