SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions. (June 2019)

• Record Type: Journal Article
• Authors: Abiantun, Ramzi; Juefei-Xu, Felix; Prabhu, Utsav; Savvides, Marios
• Is Part Of: Pattern recognition. Volume 90(2019:Jun.)
• Page Start: 308
• Page End: 324
• Publication Date: 2019-06
• Subjects: Sparse signal recovery (SSR) -- Single-image super-resolution (SSR) -- Extreme low resolution; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.01.032
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This paper introduces a novel technique to extract a sparse feature vector from extreme low resolution face images. This feature vector enables us to synthesize a high-resolution face image with magnification factors up to 16x from a single input face. We show how our method is robust to noise and handles real-world low-resolution faces. The significance of this paper lies in the fact that despite its straightforward mathematical foundations (simple subspace modeling followed by a sparse feature extraction step), it yields reconstruction results that comprehensively exceed state of the art and more convoluted methods (such as deep leaning methods SRCNN and SRGAN). Moreover, our method only requires a single input face to perform super resolution. Abstract: Automatic face recognition in the wild still suffers from low-quality, low resolution, noisy, and occluded input images that can severely impact identification accuracy. In this paper, we present a novel technique to enhance the quality of such extreme low-resolution face images beyond the current state of the art. We model the correlation between high and low resolution faces in a multi-resolution pyramid and show that we can recover the original structure of an un-seen extreme low-resolution face image. By exploiting domain knowledge of the structure of the input signal and using sparse recovery optimization algorithms, we can recover a consistent sparse representation of the extreme low-resolution signal. The proposed super-resolution method is robust to noise and face alignment, and can handle extreme low-resolution faces up to 16x magnification factor with just 7 pixels between the eyes. Moreover, the formulation of the proposed algorithm allows for simultaneous occlusion removal capability, a desirable property that other super-resolution algorithms do not possess, to the best of our knowledge. Most importantly, we show that our method generalizes on real-world low-quality surveillance images, showing the potentially big impact this can have in a real-world scenario.