publication . Conference object . Other literature type . 2012

Low-complexity single-image super-resolution based on nonnegative neighbor embedding

Bevilacqua, Marco; Roumy, Aline; Guillemot, Christine; Alberi Morel, Marie-Line;
Open Access
  • Published: 01 Sep 2012
  • Publisher: British Machine Vision Association
  • Country: France
Abstract
International audience; This paper describes a single-image super-resolution (SR) algorithm based on nonnegative neighbor embedding. It belongs to the family of single-image example-based SR algorithms, since it uses a dictionary of low resolution (LR) and high resolution (HR) trained patch pairs to infer the unknown HR details. Each LR feature vector in the input image is expressed as the weighted combination of its K nearest neighbors in the dictionary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. Three key aspects are introduced in order to build a low-complexity competitive algorithm: (i)...
Subjects
free text keywords: Competitive algorithm, Computation, Pattern recognition, Superresolution, Feature vector, Embedding, k-nearest neighbors algorithm, Computer science, Artificial intelligence, business.industry, business, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing

[1] Tak-Ming Chan, Junping Zhang, Jian Pu, and Hua Huang. Neighbor embedding based super-resolution algorithm through edge detection and feature selection. Pattern Recognition Letters, 30(5):494-502, 4 2009. ISSN 0167-8655.

[2] Hong Chang, Dit-Yan Yeung, and Yimin Xiong. Super-Resolution Through Neighbor

[6] Sina Farsiu, Dirk Robinson, Michael Elad, and Peyman Milanfar. Fast and Robust Multi-Frame Super-Resolution. IEEE Transactions on Image Processing, 13(10): 1327-1344, 10 2004.

[7] Gilad Freedman and Raanan Fattal. Image and Video Upscaling from Local SelfExamples. ACM Trans. Graph., 28(3):1-10, 2010. ISSN 0730-0301.

[8] Daniel Glasner, Shai Bagon, and Michal Irani. Super-Resolution from a Single Image. In 2009 IEEE 12th International Conference on Computer Vision (ICCV), pages 349- 356, 10 2009.

[9] Daniel D. Lee and H. Sebastian Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 10 1999.

[10] Ali Mohammad-Djafari. Super-Resolution: A Short Review, A New Method Based on Hidden Markov Modeling of HR Image and Future Challenges. The Computer Journal, 52(1):126-141, 2008.

[11] K.S. Ni and T.Q. Nguyen. Image Superresolution Using Support Vector Regression. IEEE Transactions on Image Processing, 16(6):1596-1610, 6 2007. ISSN 1057-7149.

[12] Sam T. Roweis and Lawrence K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290(5500):2323-2326, 12 2000. [OpenAIRE]

[13] Yi Tang, Pingkun Yan, Yuan Yuan, and Xuelong Li. Single-image super-resolution via local learning. International Journal of Machine Learning and Cybernetics, 2:15-23, 2011.

Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Conference object . Other literature type . 2012

Low-complexity single-image super-resolution based on nonnegative neighbor embedding

Bevilacqua, Marco; Roumy, Aline; Guillemot, Christine; Alberi Morel, Marie-Line;