Reconstructing arbitrarily focused images from two differently focused images using linear filters

We present a novel filtering method for reconstructing an all-in-focus image or an arbitrarily focused image from two images that are focused differently. The method can arbitrarily manipulate the degree of blur of the objects using linear filters without segmentation. The filters are uniquely determined from a linear imaging model in the Fourier domain. An effective and accurate blur estimation method is developed. The simulation results show that the accuracy and computational time of the proposed method are improved compared with the previous iterative method and that the effects of blur estimation error on the quality of the reconstructed image are very small. The method performs well for real images acquired without visible artifacts.

[1]  Mario Bertero,et al.  Introduction to Inverse Problems in Imaging , 1998 .

[2]  Shree K. Nayar,et al.  Shape from focus: an effective approach for rough surfaces , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[3]  Ronen Basri,et al.  Separation of Transparent Layers using Focus , 2004, International Journal of Computer Vision.

[4]  Eero P. Simoncelli,et al.  Separation of transparent motion into layers using velocity-tuned mechanisms , 1994 .

[5]  Peter Lawrence,et al.  An Investigation of Methods for Determining Depth from Focus , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sebastiano Battiato,et al.  High dynamic range imaging for digital still camera: an overview , 2003, J. Electronic Imaging.

[7]  Yoav Y. Schechner,et al.  Multi-valued Images and Their Separation , 2000, Theoretical Foundations of Computer Vision.

[8]  S. B. Kang,et al.  Survey of image-based representations and compression techniques , 2003, IEEE Trans. Circuits Syst. Video Technol..

[9]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[10]  Nikolas P. Galatsanos,et al.  Digital restoration of multichannel images , 1989, IEEE Trans. Acoust. Speech Signal Process..

[11]  Michal Irani,et al.  Video indexing based on mosaic representations , 1998, Proc. IEEE.

[12]  Kiyoharu Aizawa,et al.  Registration and blur estimation methods for multiple differently focused images , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[13]  Kiyoharu Aizawa,et al.  Generation of arbitrarily focused images by using multiple differently focused images , 1998, J. Electronic Imaging.

[14]  Kiyoharu Aizawa,et al.  Producing object-based special effects by fusing multiple differently focused images , 2000, IEEE Trans. Circuits Syst. Video Technol..

[15]  Shree K. Nayar,et al.  Shape from Focus , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Naoki Asada,et al.  Seeing Behind the Scene: Analysis of Photometric Properties of Occluding Edges by the Reversed Projection Blurring Model , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Thomas S. Huang,et al.  Image blurring effects due to depth discontinuities: blurring that creates emergent image details , 1992, Image Vis. Comput..

[18]  Sam Kavusi,et al.  Computationally efficient algorithm for multifocus image reconstruction , 2003, IS&T/SPIE Electronic Imaging.

[19]  Marc Levoy,et al.  Light field rendering , 1996, SIGGRAPH.

[20]  Wolfgang Osten,et al.  Introduction to Inverse Problems in Imaging , 1999 .

[21]  Kiyoharu Aizawa,et al.  Implicit 3D Approach to Image Generation: Object-Based Visual Effects by Linear Processing of Multiple Differently Focused Images , 2000, Theoretical Foundations of Computer Vision.

[22]  Murali Subbarao,et al.  Focused image recovery from two defocused images recorded with different camera settings , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[23]  B. S. Manjunath,et al.  Multi-sensor image fusion using the wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.

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