Simultaneous image fusion and denoising with adaptive sparse representation

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.

[1]  Nannan Yu,et al.  Image Features Extraction and Fusion Based on Joint Sparse Representation , 2011, IEEE Journal of Selected Topics in Signal Processing.

[2]  Yaonan Wang,et al.  Combination of images with diverse focuses using the spatial frequency , 2001, Inf. Fusion.

[3]  Yu Liu,et al.  Multi-focus Image Fusion Based on Sparse Representation with Adaptive Sparse Domain Selection , 2013, 2013 Seventh International Conference on Image and Graphics.

[4]  Yong Jiang,et al.  Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter , 2014, IET Image Process..

[5]  Gemma Piella,et al.  A general framework for multiresolution image fusion: from pixels to regions , 2003, Inf. Fusion.

[6]  Michael Elad,et al.  A Plurality of Sparse Representations Is Better Than the Sparsest One Alone , 2009, IEEE Transactions on Information Theory.

[7]  Tania Stathaki,et al.  Image Fusion: Algorithms and Applications , 2008 .

[8]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[9]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[10]  Stavri G. Nikolov,et al.  Image fusion: Advances in the state of the art , 2007, Inf. Fusion.

[11]  Mahboob Iqbal,et al.  Unification of image fusion and super-resolution using jointly trained dictionaries and local information contents , 2012 .

[12]  G. Qu,et al.  Information measure for performance of image fusion , 2002 .

[13]  Veysel Aslantas,et al.  Fusion of multi-focus images using differential evolution algorithm , 2010, Expert Syst. Appl..

[14]  Luciano Alparone,et al.  Remote sensing image fusion using the curvelet transform , 2007, Inf. Fusion.

[15]  Yuan Yan Tang,et al.  Multi-focus image fusion based on the neighbor distance , 2013, Pattern Recognit..

[16]  Shutao Li,et al.  Simultaneous image fusion and super-resolution using sparse representation , 2013, Inf. Fusion.

[17]  Qiang Zhang,et al.  Multifocus image fusion using the nonsubsampled contourlet transform , 2009, Signal Process..

[18]  Zhongliang Jing,et al.  Evaluation of focus measures in multi-focus image fusion , 2007, Pattern Recognit. Lett..

[19]  Gao Guorong,et al.  Multi-focus image fusion based on non-subsampled shearlet transform , 2013, IET Image Process..

[20]  Shutao Li,et al.  Performance comparison of different multi-resolution transforms for image fusion , 2011, Inf. Fusion.

[21]  Minh N. Do,et al.  The Nonsubsampled Contourlet Transform: Theory, Design, and Applications , 2006, IEEE Transactions on Image Processing.

[22]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[23]  Cedric Nishan Canagarajah,et al.  Pixel- and region-based image fusion with complex wavelets , 2007, Inf. Fusion.

[24]  Zheng Liu,et al.  Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  M. Hossny,et al.  Comments on 'Information measure for performance of image fusion' , 2008 .

[26]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[27]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[28]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[29]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[30]  Vladimir S. Petrovic,et al.  Gradient-based multiresolution image fusion , 2004, IEEE Transactions on Image Processing.

[31]  Shutao Li,et al.  Multifocus image fusion using region segmentation and spatial frequency , 2008, Image Vis. Comput..

[32]  Xin Liu,et al.  A novel similarity based quality metric for image fusion , 2008, Inf. Fusion.

[33]  Rick S. Blum,et al.  A new automated quality assessment algorithm for image fusion , 2009, Image Vis. Comput..

[34]  Lei Zhang,et al.  Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization , 2010, IEEE Transactions on Image Processing.

[35]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[36]  Shutao Li,et al.  Pixel-level image fusion with simultaneous orthogonal matching pursuit , 2012, Inf. Fusion.

[37]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[38]  Shutao Li,et al.  Multifocus Image Fusion and Restoration With Sparse Representation , 2010, IEEE Transactions on Instrumentation and Measurement.

[39]  Alexander Toet,et al.  A morphological pyramidal image decomposition , 1989, Pattern Recognit. Lett..

[40]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[41]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[42]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..