A scheme for edge-based multi-focus Color image fusion

In this paper, a novel region-based multi-focus color image fusion method is proposed, which employs the focused edges extracted from the source images to obtain a fused image with better focus. At first, the edges are obtained from the source images, using two suitable edge operators (Zero-cross and Canny). Then, a block-wise region comparison is performed to extract out the focused edges which have been morphologically dilated, followed by the selection of the largest component to remove isolated points. Any discontinuity in the detected edges is removed by consulting with the edge detection output from the Canny edge operator. The best reconstructed edge image is chosen, which is later converted into a focused region. Finally, the fused image is constructed by selecting pixels from the source images with the help of a prescribed color decision map. The proposed method has been implemented and tested on a set of real 2-D multi-focus image pairs (both gray-scale and color). The algorithm has a competitive performance with respect to the recent fusion methods in terms of subjective and objective evaluation.

[1]  Yu Liu,et al.  Multi-focus image fusion with a deep convolutional neural network , 2017, Inf. Fusion.

[2]  Wang Yajie,et al.  A multi-focus color image fusion method based on edge detection , 2015, Chinese Control and Decision Conference.

[3]  Yi Liu,et al.  Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review , 2018, Inf. Fusion.

[4]  Min Huang,et al.  Multifocus image fusion method of Ripplet transform based on cycle spinning , 2014, Multimedia Tools and Applications.

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

[6]  Rabab K. Ward,et al.  Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis , 2019, IEEE Signal Processing Letters.

[7]  Panajotis Agathoklis,et al.  Multi-Exposure and Multi-Focus Image Fusion in Gradient Domain , 2016, J. Circuits Syst. Comput..

[8]  Kun Qian,et al.  Fusion of multi-focus images via a Gaussian curvature filter and synthetic focusing degree criterion. , 2018, Applied optics.

[9]  Yu Zhang,et al.  Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure , 2017, Inf. Fusion.

[10]  Baohua Zhang,et al.  Multi-focus image fusion algorithm based on focused region extraction , 2016, Neurocomputing.

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

[12]  Shutao Li,et al.  Image matting for fusion of multi-focus images in dynamic scenes , 2013, Inf. Fusion.

[13]  Gaofeng Meng,et al.  Multifocus image fusion via focus segmentation and region reconstruction , 2014, Neurocomputing.

[14]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Nikolaos Mitianoudis,et al.  Pixel-based and region-based image fusion schemes using ICA bases , 2007, Inf. Fusion.

[16]  Kung Yao,et al.  Low-Complexity 2D Direction-of-Arrival Estimation for Acoustic Sensor Arrays , 2016, IEEE Signal Processing Letters.

[17]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[18]  Mongi A. Abidi,et al.  Evaluation of sharpness measures and search algorithms for the auto focusing of high-magnification images , 2006, SPIE Defense + Commercial Sensing.

[19]  Hadi Seyedarabi,et al.  A non-reference image fusion metric based on mutual information of image features , 2011, Comput. Electr. Eng..

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

[21]  Rabab Kreidieh Ward,et al.  Image Fusion With Convolutional Sparse Representation , 2016, IEEE Signal Processing Letters.

[22]  Rabab Kreidieh Ward,et al.  Deep learning for pixel-level image fusion: Recent advances and future prospects , 2018, Inf. Fusion.

[23]  Yu-Chiang Frank Wang,et al.  Exploring Visual and Motion Saliency for Automatic Video Object Extraction , 2013, IEEE Transactions on Image Processing.

[24]  Zengchang Qin,et al.  Multifocus image fusion based on robust principal component analysis , 2013, Pattern Recognit. Lett..

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

[26]  Bo Tao,et al.  Gesture recognition based on skeletonization algorithm and CNN with ASL database , 2018, Multimedia Tools and Applications.

[27]  Ying Sun,et al.  Intelligent human computer interaction based on non redundant EMG signal , 2020, Alexandria Engineering Journal.

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

[29]  Ying Sun,et al.  Towards the sEMG hand: internet of things sensors and haptic feedback application , 2018, Multimedia Tools and Applications.

[30]  N. Aishwarya,et al.  An image fusion framework using novel dictionary based sparse representation , 2017, Multimedia Tools and Applications.

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

[32]  Haibo Wang,et al.  Fast filtering image fusion , 2017, J. Electronic Imaging.

[33]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[34]  Shesheng Gao,et al.  Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network , 2017, IEEE Access.

[35]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[36]  Gongfa Li,et al.  Grasping force prediction based on sEMG signals , 2020 .

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

[38]  Li Chen,et al.  Multi-focus image fusion using a bilateral gradient-based sharpness criterion , 2011 .

[39]  Qionghai Dai,et al.  A regional image fusion based on similarity characteristics , 2012, Signal Process..

[40]  Ajith Abraham,et al.  A survey on region based image fusion methods , 2019, Inf. Fusion.

[41]  Zhongliang Jing,et al.  Multi-focus image fusion using pulse coupled neural network , 2007, Pattern Recognit. Lett..

[42]  Shutao Li,et al.  Image Fusion With Guided Filtering , 2013, IEEE Transactions on Image Processing.

[43]  Changxiang Wang,et al.  Research on ecological logistics evaluation model based on BCPSGA-BP neural network , 2019, Multimedia Tools and Applications.

[44]  Zheng Liu,et al.  PERFORMANCE ASSESSMENT OF COMBINATIVE PIXEL-LEVEL IMAGE FUSION BASED ON AN ABSOLUTE FEATURE MEASUREMENT , 2007 .