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Relevant bibliographies by topics / Face super-resolution

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Journal articles
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Academic literature on the topic 'Face super-resolution'

Author: Grafiati

Published: 28 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Face super-resolution"

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Kui Jia and Shaogang Gong. "Generalized Face Super-Resolution." IEEE Transactions on Image Processing 17, no. 6 (June 2008): 873–86. http://dx.doi.org/10.1109/tip.2008.922421.

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Xin, Jingwei, Nannan Wang, Xinrui Jiang, Jie Li, Xinbo Gao, and Zhifeng Li. "Facial Attribute Capsules for Noise Face Super Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12476–83. http://dx.doi.org/10.1609/aaai.v34i07.6935.

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Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.

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Kanakaraj, Sithara, V. K. Govindan, and Saidalavi Kalady. "Face Super Resolution: A Survey." International Journal of Image, Graphics and Signal Processing 9, no. 5 (May 8, 2017): 54–67. http://dx.doi.org/10.5815/ijigsp.2017.05.06.

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Liu, Zhi-Song, Wan-Chi Siu, and Yui-Lam Chan. "Reference Based Face Super-Resolution." IEEE Access 7 (2019): 129112–26. http://dx.doi.org/10.1109/access.2019.2934078.

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Chen, Jin, Jun Chen, Zheng Wang, Chao Liang, and Chia-Wen Lin. "Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 645–49. http://dx.doi.org/10.1109/lsp.2020.2986942.

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ZHANG, Di, and Jia-Zhong HE. "Feature Space Based Face Super-resolution Reconstruction." Acta Automatica Sinica 38, no. 7 (2012): 1145. http://dx.doi.org/10.3724/sp.j.1004.2012.01145.

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An, Le, and Bir Bhanu. "Face image super-resolution using 2D CCA." Signal Processing 103 (October 2014): 184–94. http://dx.doi.org/10.1016/j.sigpro.2013.10.004.

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Lu, Tao, Lanlan Pan, Yingjie Guan, and Kangli Zeng. "Face Super-Resolution by Deep Collaborative Representation." Journal of Computer-Aided Design & Computer Graphics 31, no. 4 (2019): 596. http://dx.doi.org/10.3724/sp.j.1089.2019.17323.

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Gunturk, B. K., A. U. Batur, Y. Altunbasak, M. H. Hayes, and R. M. Mersereau. "Eigenface-domain super-resolution for face recognition." IEEE Transactions on Image Processing 12, no. 5 (May 2003): 597–606. http://dx.doi.org/10.1109/tip.2003.811513.

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Chen, Chaofeng, Dihong Gong, Hao Wang, Zhifeng Li, and Kwan-Yee K. Wong. "Learning Spatial Attention for Face Super-Resolution." IEEE Transactions on Image Processing 30 (2021): 1219–31. http://dx.doi.org/10.1109/tip.2020.3043093.

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Dissertations / Theses on the topic "Face super-resolution"

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Arachchige, Somi Ruwan Budhagoda. "Face recognition in low resolution video sequences using super resolution /." Online version of thesis, 2008. http://hdl.handle.net/1850/7770.

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Roeder, James Roger. "Assessment of super-resolution for face recognition from very-low resolution images." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Lin, Frank Chi-Hao. "Super-resolution image processing with application to face recognition." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16703/.

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Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. The low-resolution images that differ by a sub-pixel shift contain complementary information as they are different "snapshots" of the same scene. Once geometrically registered onto a common high-resolution grid, they can be merged into a single image with higher resolution. As super-resolution is a computationally intensive process, traditional reconstruction-based super-resolution methods simplify the problem by restricting the correspondence between low-resolution frames to global motion such as translational and affine transformation. Surveillance footage however, consists of independently moving non-rigid objects such as faces. Applying global registration methods result in registration errors that lead to artefacts that adversely affect recognition. The human face also presents additional problems such as selfocclusion and reflectance variation that even local registration methods find difficult to model. In this dissertation, a robust optical flow-based super-resolution technique was proposed to overcome these difficulties. Real surveillance footage and the Terrascope database were used to compare the reconstruction quality of the proposed method against interpolation and existing super-resolution algorithms. Results show that the proposed robust optical flow-based method consistently produced more accurate reconstructions. This dissertation also outlines a systematic investigation of how super-resolution affects automatic face recognition algorithms with an emphasis on comparing reconstruction- and learning-based super-resolution approaches. While reconstruction-based super-resolution approaches like the proposed method attempt to recover the aliased high frequency information, learning-based methods synthesise them instead. Learning-based methods are able to synthesise plausible high frequency detail at high magnification ratios but the appearance of the face may change to the extent that the person no longer looks like him/herself. Although super-resolution has been applied to facial imagery, very little has been reported elsewhere on measuring the performance changes from super-resolved images. Intuitively, super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy. This is the first study to comprehensively investigate the effect of super-resolution on face recognition. Since super-resolution is a computationally intensive process it is important to understand the benefits in relation to the trade-off in computations. A framework for testing face recognition algorithms with multi-resolution images was proposed, using the XM2VTS database as a sample implementation. Results show that super-resolution offers a small improvement over bilinear interpolation in recognition performance in the absence of noise and that super-resolution is more beneficial when the input images are noisy since noise is attenuated during the frame fusion process.

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Ye, Getian Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Image registration and super-resolution mosaicing." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2005. http://handle.unsw.edu.au/1959.4/38653.

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This thesis presents new approaches to image registration and super-resolution mosaicing as well as their applications. Firstly, a feature-based image registration method is proposed for a multisensor surveillance system that consists of an optical camera and an infrared camera. By integrating a non-rigid object tracking technique into this method, a novel approach to simultaneous object tracking and multisensor image registration is proposed. Based on the registration and fusion of multisensor information, automatic face detection is greatly improved. Secondly, some extensions of a gradient-based image registration method, called inverse compositional algorithm, are proposed. These extensions include cumulative multi-image registration and the incorporation of illumination change and lens distortion correction. They are incorporated into the framework of the original algorithm in a consistent manner and efficiency can still be achieved for multi-image registration with illumination and lens distortion correction. Thirdly, new super-resolution mosaicing algorithms are proposed for multiple uncompressed and compressed images. Considering the process of image formation, observation models are introduced to describe the relationship between the superresolution mosaic image and the uncompressed and compressed low-resolution images. To improve the performance of super-resolution mosaicing, a wavelet-based image interpolation technique and an approach to adaptive determination of the regularization parameter are presented. For compressed images, a spatial-domain algorithm and a transform-domain algorithm are proposed. All the proposed superresolution mosaicing algorithms are robust against outliers. They can produce superresolution mosaics and reconstructed super-resolution images with improved subjective quality. Finally, new techniques for super-resolution sprite generation and super-resolution sprite coding are proposed. Considering both short-term and long-term motion influences, an object-based image registration method is proposed for handling long image sequences. In order to remove the influence of outliers, a robust technique for super-resolution sprite generation is presented. This technique produces sprite images and reconstructed super-resolution images with high visual quality. Moreover, it provides better reconstructed low-resolution images compared with low-resolution sprite generation techniques. Due to the advantages of the super-resolution sprite, a super-resolution sprite coding technique is also proposed. It achieves high coding efficiency especially at a low bit-rate and produces both decoded low-resolution and super-resolution images with improved subjective quality. Throughout this work, the performance of all the proposed algorithms is evaluated using both synthetic and real image sequences.

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Qu, Chengchao [Verfasser]. "Facial Texture Super-Resolution by Fitting 3D Face Models / Chengchao Qu." Karlsruhe : KIT Scientific Publishing, 2018. http://www.ksp.kit.edu.

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Peyrard, Clément. "Single image super-resolution based on neural networks for text and face recognition." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI083/document.

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Cette thèse porte sur les méthodes de super-résolution (SR) pour l’amélioration des performances des systèmes de reconnaissance automatique (OCR, reconnaissance faciale). Les méthodes de Super-Résolution (SR) permettent de générer des images haute résolution (HR) à partir d’images basse résolution (BR). Contrairement à un rééchantillonage par interpolation, elles restituent les hautes fréquences spatiales et compensent les artéfacts (flou, crénelures). Parmi elles, les méthodes d’apprentissage automatique telles que les réseaux de neurones artificiels permettent d’apprendre et de modéliser la relation entre les images BR et HR à partir d’exemples. Ce travail démontre l’intérêt des méthodes de SR à base de réseaux de neurones pour les systèmes de reconnaissance automatique. Les réseaux de neurones à convolutions sont particulièrement adaptés puisqu’ils peuvent être entraînés à extraire des caractéristiques non-linéaires bidimensionnelles pertinentes tout en apprenant la correspondance entre les espaces BR et HR. Sur des images de type documents, la méthode proposée permet d’améliorer la précision en reconnaissance de caractère de +7.85 points par rapport à une simple interpolation. La création d’une base d’images annotée et l’organisation d’une compétition internationale (ICDAR2015) ont souligné l’intérêt et la pertinence de telles approches. Pour les images de visages, les caractéristiques faciales sont cruciales pour la reconnaissance automatique. Une méthode en deux étapes est proposée dans laquelle la qualité de l’image est d’abord globalement améliorée, pour ensuite se focaliser sur les caractéristiques essentielles grâce à des modèles spécifiques. Les performances d’un système de vérification faciale se trouvent améliorées de +6.91 à +8.15 points. Enfin, pour le traitement d’images BR en conditions réelles, l’utilisation de réseaux de neurones profonds permet d’absorber la variabilité des noyaux de flous caractérisant l’image BR, et produire des images HR ayant des statistiques naturelles sans connaissance du modèle d’observation exact
This thesis is focussed on super-resolution (SR) methods for improving automatic recognition system (Optical Character Recognition, face recognition) in realistic contexts. SR methods allow to generate high resolution images from low resolution ones. Unlike upsampling methods such as interpolation, they restore spatial high frequencies and compensate artefacts such as blur or jaggy edges. In particular, example-based approaches learn and model the relationship between low and high resolution spaces via pairs of low and high resolution images. Artificial Neural Networks are among the most efficient systems to address this problem. This work demonstrate the interest of SR methods based on neural networks for improved automatic recognition systems. By adapting the data, it is possible to train such Machine Learning algorithms to produce high-resolution images. Convolutional Neural Networks are especially efficient as they are trained to simultaneously extract relevant non-linear features while learning the mapping between low and high resolution spaces. On document text images, the proposed method improves OCR accuracy by +7.85 points compared with simple interpolation. The creation of an annotated image dataset and the organisation of an international competition (ICDAR2015) highlighted the interest and the relevance of such approaches. Moreover, if a priori knowledge is available, it can be used by a suitable network architecture. For facial images, face features are critical for automatic recognition. A two step method is proposed in which image resolution is first improved, followed by specialised models that focus on the essential features. An off-the-shelf face verification system has its performance improved from +6.91 up to +8.15 points. Finally, to address the variability of real-world low-resolution images, deep neural networks allow to absorb the diversity of the blurring kernels that characterise the low-resolution images. With a single model, high-resolution images are produced with natural image statistics, without any knowledge of the actual observation model of the low-resolution image

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Al-Hassan, Nadia. "Mathematically inspired approaches to face recognition in uncontrolled conditions : super resolution and compressive sensing." Thesis, University of Buckingham, 2014. http://bear.buckingham.ac.uk/6/.

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Face recognition systems under uncontrolled conditions using surveillance cameras is becoming essential for establishing the identity of a person at a distance from the camera and providing safety and security against terrorist, attack, robbery and crime. Therefore, the performance of face recognition in low-resolution degraded images with low quality against images with high quality/and of good resolution/size is considered the most challenging tasks and constitutes focus of this thesis. The work in this thesis is designed to further investigate these issues and the following being our main aim: “To investigate face identification from a distance and under uncontrolled conditions by primarily addressing the problem of low-resolution images using existing/modified mathematically inspired super resolution schemes that are based on the emerging new paradigm of compressive sensing and non-adaptive dictionaries based super resolution.” We shall firstly investigate and develop the compressive sensing (CS) based sparse representation of a sample image to reconstruct a high-resolution image for face recognition, by taking different approaches to constructing CS-compliant dictionaries such as Gaussian Random Matrix and Toeplitz Circular Random Matrix. In particular, our focus is on constructing CS non-adaptive dictionaries (independent of face image information), which contrasts with existing image-learnt dictionaries, but satisfies some form of the Restricted Isometry Property (RIP) which is sufficient to comply with the CS theorem regarding the recovery of sparsely represented images. We shall demonstrate that the CS dictionary techniques for resolution enhancement tasks are able to develop scalable face recognition schemes under uncontrolled conditions and at a distance. Secondly, we shall clarify the comparisons of the strength of sufficient CS property for the various types of dictionaries and demonstrate that the image-learnt dictionary far from satisfies the RIP for compressive sensing. Thirdly, we propose dictionaries based on the high frequency coefficients of the training set and investigate the impact of using dictionaries on the space of feature vectors of the low-resolution image for face recognition when applied to the wavelet domain. Finally, we test the performance of the developed schemes on CCTV images with unknown model of degradation, and show that these schemes significantly outperform existing techniques developed for such a challenging task. However, the performance is still not comparable to what could be achieved in controlled environment, and hence we shall identify remaining challenges to be investigated in the future.

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Svorad, Adam. "Zvýšení kvality v obrazu obličeje s použitím sekvence snímků." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442384.

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Diplomova praca sa zameriava na oblast zaostrovania obrazkov tvari. V teoretickej casti prace budu prezentovane moderne metody zaostrovania obrazkov pomocou jedineho obrazku a metody editacie obrazkov. Prakticka cast sa zameria na pristupy rekonstrukcie obrazkov zo sekvencie poskodenych obrazkov. Viacere modely neuronovych sieti so vstupom pre viacero obrazkov budu zhotovene a vyhodnotene. Alternativny pristup v podobe balika nastrojov na editaciu obrazkov bude taktiez predstaveny. Tieto nastroje budu vyuzivat najmodernejsie pristupy k editacii obrazkov s cielom spojit vizualne prvky tvari zo vstupnej sekvencie obrazkov do jedneho finalneho vystupu. V zavere prace budu vsetky metody navzajom porovnane.

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Rajnoha, Martin. "Určování podobnosti objektů na základě obrazové informace." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-437979.

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Monitoring of public areas and their automatic real-time processing became increasingly significant due to the changing security situation in the world. However, the problem is an analysis of low-quality records, where even the state-of-the-art methods fail in some cases. This work investigates an important area of image similarity – biometric identification based on face image. The work deals primarily with the face super-resolution from a sequence of low-resolution images and it compares this approach to the single-frame methods, that are still considered as the most accurate. A new dataset was created for this purpose, which is directly designed for the multi-frame face super-resolution methods from the low-resolution input sequence, and it is of comparable size with the leading world datasets. The results were evaluated by both a survey of human perception and defined objective metrics. A hypothesis that multi-frame methods achieve better results than single-frame methods was proved by a comparison of both methods. Architectures, source code and the dataset were released. That caused a creation of the basis for future research in this field.

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Lee, Kuan-I., and 李冠毅. "Enhancing Face Detection with Super Resolution." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ttag7s.

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碩士
國立清華大學
通訊工程研究所
106
Object detection has been a hot research topic for many years to date. Among various task we have achieved so far, recent state-of-the-art face detection through deep learning architecture method have proven to be very robust to many kinds of image scenario. Yet when it comes to very tiny faces, typically faces that has no more than 10~20 in pixel sizes, still remains a challenging task for face detection. Many methods have been proposed to address tiny face detection problem. Most of them are based on image-pyramid or feature-pyramid method. By leveraging multiple sizes in spatial domain or receptive field in convolutional feature layers, it’s a common believe that we can extract more information and thus getting a better result in face detection. Both of these method have their pros and cons. In this paper, we aim our goal to the tiny face detection and introduce a method that utilize super resolution technique which can increase the sample candidate from the existing detection pipeline and enhance the performance without additional image or feature pyramids. We provided detailed comparison and visualization in our work, and the result have shown performance boost in face detection task.

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Book chapters on the topic "Face super-resolution"

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Pan, Gang, Shi Han, Zhaohui Wu, and Yueming Wang. "Super-Resolution of 3D Face." In Computer Vision – ECCV 2006, 389–401. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744047_30.

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Dong, Ning, Xiaoguang Li, Jiafeng Li, and Li Zhuo. "Face Super-Resolution via Discriminative-Attributes." In Pattern Recognition and Computer Vision, 487–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_41.

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Liu, Jing, Guangda Su, Xiaolong Ren, and Jiansheng Chen. "Human Face Super-Resolution Based on NSCT." In Computer Vision – ACCV 2012, 680–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37444-9_53.

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Gao, Guangwei, and Jian Yang. "Sparse Representation Based Face Image Super-Resolution." In Intelligent Science and Intelligent Data Engineering, 303–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_39.

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Zhuo, Hui, and Kin-Man Lam. "Wavelet-Based Eigentransformation for Face Super-Resolution." In Advances in Multimedia Information Processing - PCM 2010, 226–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15696-0_21.

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Zheng, Jun, and Olac Fuentes. "A Stochastic Method for Face Image Super-Resolution." In Advances in Visual Computing, 762–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10331-5_71.

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Yu, Xin, Basura Fernando, Bernard Ghanem, Fatih Porikli, and Richard Hartley. "Face Super-Resolution Guided by Facial Component Heatmaps." In Computer Vision – ECCV 2018, 219–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01240-3_14.

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Huang, Kebin, Ruimin Hu, Junjun Jiang, Zhen Han, and Feng Wang. "Face Image Super-Resolution Through Improved Neighbor Embedding." In MultiMedia Modeling, 409–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27671-7_34.

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Fang, Yuchun, Qicai Ran, and Yifan Li. "Fractal Residual Network for Face Image Super-Resolution." In Artificial Neural Networks and Machine Learning – ICANN 2020, 15–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61609-0_2.

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Hu, Xiaobin, Wenqi Ren, John LaMaster, Xiaochun Cao, Xiaoming Li, Zechao Li, Bjoern Menze, and Wei Liu. "Face Super-Resolution Guided by 3D Facial Priors." In Computer Vision – ECCV 2020, 763–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58548-8_44.

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Conference papers on the topic "Face super-resolution"

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Abello, Antonio Augusto, and R. Hirata. "Optimizing Super Resolution for Face Recognition." In 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2019. http://dx.doi.org/10.1109/sibgrapi.2019.00034.

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Jia, K., S. Gong, and A. P. Leung. "Coupling Face Registration and Super-Resolution." In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.47.

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Bilgazyev, Emil, Boris Efraty, Shishir K. Shah, and Ioannis A. Kakadiaris. "Improved face recognition using super-resolution." In 2011 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2011. http://dx.doi.org/10.1109/ijcb.2011.6117554.

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Hu, Shuowen, Robert Maschal, S. Susan Young, Tsai Hong Hong, and Jonathon P. Phillips. "Super-resolution benefit for face recognition." In SPIE Defense, Security, and Sensing, edited by Sárka O. Southern, Kevin N. Montgomery, Carl W. Taylor, Bernhard H. Weigl, B. V. K. Vijaya Kumar, Salil Prabhakar, and Arun A. Ross. SPIE, 2011. http://dx.doi.org/10.1117/12.882956.

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Liu Li and YiDing Wang. "Face super-resolution using a hybrid model." In 2008 9th International Conference on Signal Processing (ICSP 2008). IEEE, 2008. http://dx.doi.org/10.1109/icosp.2008.4697334.

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Yao, Yi, Besma Abidi, Nathan D. Kalka, Natalia Schmid, and Mongi Abidi. "Super-resolution for high magnification face images." In Defense and Security Symposium, edited by Salil Prabhakar and Arun A. Ross. SPIE, 2007. http://dx.doi.org/10.1117/12.720113.

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Wheeler, Frederick W., Xiaoming Liu, and Peter H. Tu. "Multi-Frame Super-Resolution for Face Recognition." In 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. IEEE, 2007. http://dx.doi.org/10.1109/btas.2007.4401949.

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Wang, Zhifei, and Zhenjiang Miao. "Feature-based super-resolution for face recognition." In 2008 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2008. http://dx.doi.org/10.1109/icme.2008.4607748.

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Huang, Hua, Ning Wu, Xin Fan, and Chun Qi. "Face image super resolution by linear transformation." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5651255.

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Nasrollahi, Kamal, and Thomas B. Moeslund. "Hybrid super resolution using refined face logs." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586769.

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