Compressed face hallucination

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.