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Robust Face Hallucination Using Quantization-Adaptive Dictionaries

Reuben Farrugia 1 Christine Guillemot 2
2 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Existing face hallucination methods are optimized to super-resolve uncompressed images and are not able to handle the distortions caused by compression. This work presents a new dictionary construction method which jointly models both distortions caused by down-sampling and compression. The resulting dictionaries are then used to make three face super-resolution methods more robust to compression. Experimental results show that the proposed dictionary construction method generates dictionaries which are more representative of the low-quality face image being restored and makes the extended face hallucination methods more robust to compression. These experiments demonstrate that the proposed robust face hallucination methods can achieve Peak Signal-to-Noise Ratio (PSNR) gains between 2–4.48dB and recognition improvement between 2.9–8.1% compared with the low-quality image and outperforming traditional super-resolution methods in most cases.
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https://hal.inria.fr/hal-01388972
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Submitted on : Tuesday, November 15, 2016 - 1:30:52 PM
Last modification on : Friday, March 6, 2020 - 1:03:30 AM
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  • HAL Id : hal-01388972, version 1

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Reuben Farrugia, Christine Guillemot. Robust Face Hallucination Using Quantization-Adaptive Dictionaries. IEEE International Conference on Image Processing, Sep 2016, Phoenix, United States. pp.5. ⟨hal-01388972⟩

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