Multi-focus image fusion via NSST with non-fixed base dictionary learning

Multi-focus image fusion method can fuse images taken from the same view point with different focal settings, and obtain an image with every object in focus. In this paper, a novel multi-focus image fusion via non-subsampled shearlet transform (NSST) with non-fixed base dictionary learning is presented. First, low frequency coefficients and high frequency coefficients are obtained by NSST. Then, a new strategy, which can enhance the information of spatial detail for the fused image is proposed to process two different coefficients. The low frequency coefficients are fused via a non-fixed base dictionary, which makes the K-SVD algorithm more efficient, and the high frequency coefficients are fused with spatial frequency, which is effective in the fused image. Experiment results demonstrate that the results of proposed method obtain more spatial details and have almost zero residuals compared with several conventional methods in terms of both visual quality and objective measurements.

[1]  Jun Sun,et al.  Robust Sparse Representation Combined With Adaptive PCNN for Multifocus Image Fusion , 2018, IEEE Access.

[2]  Shadrokh Samavi,et al.  Multi-focus image fusion using dictionary-based sparse representation , 2015, Inf. Fusion.

[3]  Jason Weston,et al.  Curriculum learning , 2009, ICML '09.

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

[5]  Yong Yang,et al.  Multi-Focus Image Fusion Based on a Non-Fixed-Base Dictionary and Multi-Measure Optimization , 2019, IEEE Access.

[6]  Shutao Li,et al.  Pixel-level image fusion: A survey of the state of the art , 2017, Inf. Fusion.

[7]  Yu Liu,et al.  Simultaneous image fusion and denoising with adaptive sparse representation , 2015, IET Image Process..

[8]  Z. Babic,et al.  Multifocus image fusion based on the first level of empirical mode decomposition , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

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

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

[11]  LiShutao,et al.  Pixel-level image fusion , 2017 .

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

[13]  Lu Ding,et al.  Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion , 2016, Neurocomputing.

[14]  Ming Dai,et al.  Multifocus color image fusion based on quaternion curvelet transform. , 2012, Optics express.

[15]  Alexander Toet,et al.  Image fusion by a ration of low-pass pyramid , 1989, Pattern Recognit. Lett..

[16]  Yu Liu,et al.  A general framework for image fusion based on multi-scale transform and sparse representation , 2015, Inf. Fusion.

[17]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[18]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[19]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[20]  Pan Lin,et al.  Multifocus Image Fusion Based on NSCT and Focused Area Detection , 2014, IEEE Sensors Journal.

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

[22]  Bo Li,et al.  Multifocus image fusion via fixed window technique of multiscale images and non-local means filtering , 2017, Signal Process..