Multifocus image fusion using artificial neural networks

Optical lenses, particularly those with long focal lengths, suffer from the problem of limited depth of field. Consequently, it is often difficult to obtain good focus for all objects in the picture. One possible solution is to take several pictures with different focus points, and then combine them together to form a single image. This paper describes an application of artificial neural networks to this pixel level multifocus image fusion problem based on the use of image blocks. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach, particularly when there is a movement in the objects or misregistration of the source images.

[1]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[2]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[3]  Laure J. Chipman,et al.  Wavelets and image fusion , 1995, Proceedings., International Conference on Image Processing.

[4]  Fred J. Taylor,et al.  Image fusion using steerable dyadic wavelet transform , 1995, Proceedings., International Conference on Image Processing.

[5]  D. Yocky.  Image merging and data fusion by means of the discrete two-dimensional wavelet transform , 1995 .

[6]  Ahmad R. Mirzai,et al.  Application of neural networks to the segmentation of MRI: comparison of different networks , 1997, Optics & Photonics.

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

[8]  R. Bellman,et al.  V. Adaptive Control Processes , 1964 .

[9]  S. Marshall,et al.  Multiresolution morphological fusion of MR and CT images of the human brain , 1995 .

[10]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

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

[12]  Peter J. Burt,et al.  Enhanced image capture through fusion , 1993, 1993 (4th) International Conference on Computer Vision.

[13]  W. Brent Seales,et al.  Everywhere-in-focus image fusion using controlablle cameras , 1996, Other Conferences.

[14]  Stewart Marshall,et al.  Multires-olution morphological fusion of mr and ct images of the human brain , 1994 .

[15]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

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

[17]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[18]  Rick S. Blum,et al.  A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application , 1999, Proc. IEEE.

[19]  Alexander Toet,et al.  Merging thermal and visual images by a contrast pyramid , 1989 .