Multi-focal image fusion using degree of focus and fuzzy logic

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• 作者：M.A. Rahman ; ^{md.arifur.rahman056@student.unsw.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; S. LiuAuthor Vitae ; C.Y. WongAuthor Vitae ; S.C.F. LinAuthor Vitae ; S.C. LiuAuthor Vitae ; N.M. KwokAuthor Vitae
• 关键词：Multi-focal ; Image fusion ; Degree of focus ; Sum of gradient (SoG) ; Alternate sum modified Laplacian (aSML)
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：60
• 期：Complete
• 页码：1-19
• 全文大小：8076 K

文摘

The continuous advancement in the field of imaging sensor necessitates the development of an efficient image fusion technique. The multi-focal image fusion extracts the in-focus information from the source images to construct a single composite image with increased depth-of-field. Traditionally, the information in multi-focal images is divided into two categories: in-focus and out-of-focus data. Instead of using a binary focus map, in this work we calculate the degree of focus for each source image using fuzzy logic. The fused image is then generated based on the weighted sum of this information. An initial focus tri-state map is built for each input image by using spatial frequency and a proposed focus measure named as alternate sum modified Laplacian. For the cases where these measures indicate different source images to contain focused pixel or have equal strength, another focus measure based on sum of gradient is employed to calculate the degree of focus in a fuzzy inference system. Finally, the fused image is computed from the weights determined by the degree of focus map of each image. The proposed algorithm is designed to fuse two source images, whereas fusion of multiple input images can be performed by fusing a source image with the fusion output of the previous input group. The comparison of the proposed method with several transform and pixel domain based techniques are conducted in terms of both subjective visual assessment and objective quantitative evaluation. Experimental results demonstrate that our method can be competitive with or even outperforms the methods in comparison.