In this paper, we propose an algorithm to hallucinate faces in the JPEG compressed domain, which has not been well addressed in the literature. The proposed approach hallucinates compressed face images through an exemplar-based framework and solves two main problems. First, image noise introduced by JPEG compression is exacerbated through the super-resolution process. We present a novel formulation for face hallucination that uses the JPEG quantization intervals as constraints to recover the feasible intensity values from each image patch of a low-resolution input. Second, existing face hallucination methods are sensitive to noise contained in the compressed images. We regularize the compression noise caused by block discrete cosine transform coding, and reconstruct high-resolution images with the proposed gradient-guided total variation. Numerous experimental results show that the proposed algorithm generates favorable results than the combination of state-of-the-art face hallucination and de-noising algorithms.
|Title of host publication||2014 IEEE International Conference on Image Processing, ICIP 2014|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2014 Jan 28|
|Name||2014 IEEE International Conference on Image Processing, ICIP 2014|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition