Image resolution enhancement using statistical estimation in wavelet domain

Abstract The goal of medical image resolution enhancement is to reconstruct a higher-resolution image from its lower-resolution counterpart. This paper proposes a Bayesian approach in the wavelet domain by exploiting a Bayesian inference framework to mathematically formulate the image interpolation problem. Furthermore, the proposed approach jointly estimates both the unknown wavelet coefficients of the high-resolution image and the unknown parameters of the statistical model for wavelet coefficients. Experiments are conducted to demonstrate the superior performance of the proposed approach.

[1]  Zhou Wang,et al.  Information Content Weighting for Perceptual Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[2]  Kai-Kuang Ma,et al.  A state-space super-resolution approach for video reconstruction , 2009, Signal Image Video Process..

[3]  Mei-Juan Chen,et al.  Wavelet-based Interpolation Scheme for Resolution Enhancement of Medical Images , 2009, J. Signal Process. Syst..

[4]  Hwang Soo Lee,et al.  Adaptive image interpolation based on local gradient features , 2004, IEEE Signal Process. Lett..

[5]  R. Keys.  Cubic convolution interpolation for digital image processing , 1981 .

[6]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[7]  Jing Tian,et al.  Wavelet-Based Image Interpolation Using a Three-Component Exponential Mixture Model , 2008, 2008 Congress on Image and Signal Processing.

[8]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[9]  Lei Zhang,et al.  An edge-guided image interpolation algorithm via directional filtering and data fusion , 2006, IEEE Transactions on Image Processing.

[10]  Il Kyu Eom,et al.  Image interpolation based on inter-scale dependency in wavelet domain , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[11]  Kai-Kuang Ma,et al.  A survey on super-resolution imaging , 2011, Signal Image Video Process..

[12]  Jing Tian,et al.  Wavelet-Based Image Interpolation Using a Three-Component Exponential Mixture Model , 2008 .

[13]  Alptekin Temizel,et al.  Wavelet domain image resolution enhancement , 2006 .

[14]  Stéphane Mallat,et al.  Super-Resolution With Sparse Mixing Estimators , 2010, IEEE Transactions on Image Processing.

[15]  Hsieh Hou,et al.  Cubic splines for image interpolation and digital filtering , 1978 .

[16]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[17]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.

[18]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[19]  Dimitris Anastassiou,et al.  Subpixel edge localization and the interpolation of still images , 1995, IEEE Trans. Image Process..

[20]  Kai-Kuang Ma,et al.  Stochastic super-resolution image reconstruction , 2010, J. Vis. Commun. Image Represent..

[21]  Pascal Getreuer.  Contour stencils for edge-adaptive image interpolation , 2009, Electronic Imaging.

[22]  Thomas W. Parks,et al.  Image interpolation using wavelet based hidden Markov trees , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[23]  S. Xie,et al.  A Wavelet-Domain Bayesian Image Enhancement Approach in Distributed Video Sensor Network , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[24]  Kai-Kuang Ma,et al.  A MCMC approach for Bayesian super-resolution image reconstruction , 2005, IEEE International Conference on Image Processing 2005.

[25]  Jing Tian,et al.  Image despeckling using a non-parametric statistical model of wavelet coefficients , 2011, Biomed. Signal Process. Control..