Contourlet Transform based Multi-scale Fusion Network for Face Hallucination

Recently, Convolutional neural network (CNN) has achieved great success in the field of face hallucination. However, such approaches usually generate blurry and over-smoothed Super Resolution (SR) results, and the performance suffers from degradation when super-resolve a very Low Resolution (LR) fac...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1335; no. 1; pp. 12008 - 12013
Main Authors Wei, W, Feng, G Q, Cui, D L
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.10.2019
Subjects
Artificial neural networks
Hallucinations
Image reconstruction
Image resolution
Performance degradation
Physics
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Summary:Recently, Convolutional neural network (CNN) has achieved great success in the field of face hallucination. However, such approaches usually generate blurry and over-smoothed Super Resolution (SR) results, and the performance suffers from degradation when super-resolve a very Low Resolution (LR) face image. To solve these problems, this paper proposes an contourlet transform based accurate CNN architecture, namely Multi-scale Fusion CNN (MSFC), which are able to reconstruct a High Resolution (HR) face image from a very low resolution input. First of all, we present multi-scale fusion CNN (MSFC) to fully detect and exploit features from LR inputs. And then, we formulate the SR problem as the prediction of contourlet transform coefficients, which is able to make MSFC further capture the texture details for super-resolve face images. Extensive qualitative and quantitative experiments show that the proposed method is capable of preserving details and achieves superior SR performance compared to the state-of-the-art methods.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1335/1/012008