Face Hallucination

Convolutional Sparse Coding for Face Hallucination

In a previous blog post, Face Hallucination was introduced as a specialization of Image Super Resolution (SR) that also refers to Face Super Resolution (FSR). We’ve also listed the constraints postulated by Liu et al.[4] for a successful FH. Let’s list them here once again for the sake of completeness:

1. Sanity constraint: the target HR image should be very close to the input LR image when smoothed and down-sampled.

2. Global constraint: the target HR image should have the common characteristics of human faces, e.g., possessing a mouth and a nose, being symmetrical, etc.

3. Local constraint: the target HR image should have the specific characteristics of the original LR face image, with photorealistic local features.

In this blog post, we propose a novel approach to FH based on Gu et al. [1] in which they argued that a Convolutional Sparse Coding (CSC) would achieve superior performance to Yang et al. [5] Sparse Coding Super Resolution (ScSR). Hence, we apply the CSC techniques in the context of Liu et al. [4] framework for FH.

As a side note, using Deep Convolutional Networks may surpass Sparse Representation approaches to Image Super Resolution. As suggested by [6].

References:

1. S. Gu, W. Zuo, Q. Xie, D. Meng, X. Feng, L. Zhang, “Convolutional Sparse Coding for Image Super-resolution,” in ICCV 2015. [paper] [code]
2. Learning a Deep Convolutional Network for Image Super-Resolution
4. Ce Liu, Heung-Yeung Shum & William T. Freeman. Face Hallucination: Theory and Practice. International Journal of Computer Vision, Volume 75 Issue 1, October 2007, Pages 115-134.
5. Jianchao Yang, Hao Tang, Yi Ma & Huang, T. Face hallucination VIA sparse coding. Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on 12-15 Oct. 2008, Page(s): 1264 – 1267.
6. Image Super-Resolution Using Deep Convolutional Networks
Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html

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