Abstract
A novel multi-focus polychromatic image fusion algorithm based on filtering in the frequency domain using fast Fourier transform (FFT) and synthesis in the space domain (FFDSSD) is presented in this paper. First, the original multi-focus images are transformed into their frequency data by FFT for easy and accurate clarity determination. Then a Gaussian low-pass filter is used to filter the high frequency information corresponding to the image saliencies. After an inverse FFT, the filtered images are obtained. The deviation between the filtered images and the original ones, representing the clarity of the image, is used to select the pixels from the multi-focus images to reconstruct a completely focused image. These operations in space domain preserve the original information as much as possible and are relatively insensitive to misregistration scenarios with respect to transform domain methods. The polychromatic noise is well considered and successfully avoided while the information in different chromatic channels is preserved. A natural, nice-looking fused microscopic image for human visual evaluations is obtained in a dedicated experiment. The experimental results indicate that the proposed algorithm has a good performance in objective quality metrics and runtime efficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Burt, P., Andelson, E.H., 1983. The Laplacian pyramid as a compact image code. IEEE Trans. Commun., 31(4):532–540. [doi:10.1109/TCOM.1983.1095851]
Burt, P., Hanna, K., Lolczynski, R., 1993. Enhanced Image Capture through Fusion. Proc. 4th Int. Conf. on Computer Vision, p.173–182. [doi:10.1109/ICCV.1993.378222]
De, I., Chanda, B., 2006. A simple and efficient algorithm for multifocus image fusion using morphological wavelets. Signal Process., 86(5):924–936. [doi:10.1016/j.sigpro.2005.06.015]
Eskicioglu, A., Fisher, P., 1995. Image quality measures and their performance. IEEE Trans. Commun., 43(12):2959–2965. [doi:10.1109/26.477498]
Forster, B., van de Ville, D., Berent, J., Sage, D., Unser, M., 2004. Complex wavelets for extended depth-of-field: a new method for the fusion of multichannel microscopy images. Microsc. Res. Techn., 65(1–2):33–42. [doi:10.1002/jemt.20092]
Gabarda, S., Cristóbal, G., 2005. On the use of a joint spatial-frequency representation for the fusion of multi-focus images. Pattern Recogn. Lett., 26(16):2572–2578. [doi:10.1016/j.patrec.2005.06.003]
Helmy, A.E., Sam, K., 2003. A computationally efficient algorithm for multifocus image reconstruction. SPIE, 5017:332–341. [doi:10.1117/12.476754]
Huang, W., Jing, Z., 2007. Multi-focus image fusion using pulse coupled neural network. Pattern Recogn. Lett., 28(9):1123–1132. [doi:10.1016/j.patrec.2007.01.013]
Li, H., Manjunath, B., Mitra, S., 1995. Multi-sensor image fusion using the wavelet transform. Graph. Models Image Process., 57(3):235–245. [doi:10.1006/gmip.1995.1022]
Li, S., James, T.K., Wang, Y., 2001. Combination of images with diverse focuses using the spatial frequency. Inf. Fus., 2(3):169–176. [doi:10.1016/S1566-2535(01)00038-0]
Liu, Q., Zhao, T., Zhang, W., Yu, F., 2008. Image restoration based on generalized minimal residual methods with antireflective boundary conditions in a wavefront coding system. Opt. Eng., 47(12):127005. [doi:10.1117/1.3050348]
Matsopoulos, G., Marshall, S., Brunt, J., 1994. Multiresolution morphological fusion of MR and CT images of the human brain. IEE Proc.-Vis. Image Signal Process., 141(3): 137–142. [doi:10.1049/ip-vis:19941184]
Oliver, R., 1996. Pixel-Level Fusion of Image Sequences Using Wavelet Frames. Proc. 16th Leeds Applied Research Workshop, p.149–154.
Pajares, G., Cruz, J., 2004. A wavelet-based image fusion tutorial. Pattern Recogn., 37(9):1855–1872. [doi:10.1016/j.patcog.2004.03.010]
Rajagopalan, A.N., Chaudhuri, S., 1997. Space-variant approaches to recovery of depth from defocused images. Comput. Vis. Image Underst., 68(3):309–329. [doi:10.1006/cviu.1997.0534]
Sroubek, F., Gabarda, S., Redondo, R., Fischer, S., Cristobal, G., 2005. Multifocus fusion with oriented windows. SPIE, 5839:264–273. [doi:10.1117/12.608399]
Toet, A., Valeton, J., van Ruyven, L., 1989. Merging thermal and visual images by a contrast pyramid. Opt. Eng., 28(7):789–792. [doi:10.1117/12.55479]
Yang, X., Yang, W., Pei, J., 2000. Different Focus Points Images Fusion Based on Wavelet Decomposition. Proc. 3rd Int. Conf. on Information Fusion, MOD3/3-8.
Zhao, H., Li, Q., Feng, H., 2008. Multi-focus color image fusion in the HIS space using the sum-modified-Laplacian and a coarse edge map. Image Vis. Comput., 26(9):1285–1295. [doi:10.1016/j.imavis.2008.03.007]
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, L., Liu, P., Liu, Yl. et al. High quality multi-focus polychromatic composite image fusion algorithm based on filtering in frequency domain and synthesis in space domain. J. Zhejiang Univ. - Sci. C 11, 365–374 (2010). https://doi.org/10.1631/jzus.C0910344
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.C0910344