Digital Image Magnification Using Gaussian-Edge Directed Interpolation
This paper presents a simple and cost effective approach for digital image magnification (DIM). DIM is used in various applications and is an enthusiastic area of research at present. The proposed technique uses Gaussian edge directed interpolation to determine the precise weights of the neighboring pixels. The standard deviation of the interpolation window determines the value of ‘σ’ for generating Gaussian kernels. Gaussian kernels preserve the original detail of the low-resolution image to produce high-resolution image of high visual quality. The experimental results show that the proposed technique is superior to other techniques qualitatively as well as quantitatively.
KeywordsDigital image magnification Gaussian kernel Gaussian sigma Weighted interpolation
This research is supported by, (1) The Industrial Strategic technology development program, 10041772, (The Development of an Adaptive Mixed-Reality Space based on Interactive Architecture) funded by the Ministry of Knowledge Economy (MKE, Korea), and (2) The MKE (The Ministry of Knowledge Economy), Korea, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency)” (NIPA-2012- H0502-12-1013).
- 1.Jurio A, Pagola M, Mesiar R, Beliakov G, Bustince H (2011) Image magnification using interval information. IEEE Trans Image Process, 20(11):3112–3123Google Scholar
- 2.Amanatiadis A, Andreadis I (2009) A survey on evaluation methods for image interpolation. Meas Sci Technol 20(10):104015–104021Google Scholar
- 4.Yeon JL, Jungho Y (2010) Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans Image Process 19(10):2682–2692Google Scholar
- 5.Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edn. AmazonGoogle Scholar
- 6.Shapiro LG, Stockman GC (2001) Computer vision. AmazonGoogle Scholar
- 10.Wittman T (2005) Mathematical techniques for image interpolation. Department of Mathematics, University of MinnesotaGoogle Scholar
- 12.Shan Q, Li Z Jia J, Tang CK (2008) Fast image/video upsampling. ACM Transactions on Graphics (SIGGRAPH ASIA) 27:153–160Google Scholar
- 13.Hung KW, Siu WC (2009) New motion compensation model via frequency classification for fast video super-resolution. IEEE Int Conf Image ProcessGoogle Scholar
- 14.Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans on Pattern Anal Mach Intell 24:1167–1183Google Scholar
- 20.Kim KI, Kwon Y (2008) Example-based learning for single image super-resolution and JPEG artifact removal. Technical Report 173, Max Planck InstituteGoogle Scholar
- 22.He H, Siu WC (2011) Single image super resolution using Gaussian process regression. IEEE Conf Comput Vis Pattern Recognit 449–456Google Scholar