Multifocus image fusion has emerged as a main topic since the optical lenses for most widely used imaging devices have a limiting focus range. Only objects at one particular depth will be truly in focus while out-of-focus objects will become blurry. The ability to create a single image where all scene areas appear sharp is desired not only in digital photography but also in various vision-related applications. Wan et al. proposed an image fusion scheme for combining two or multiple images with different focus points to generate an all-in-focus image. Thus, the problem of fusing multifocus images is formulated as a choice of the most significant features from a sparse matrix obtained by RPCA decomposition method to form a composite feature space. The local sparse features that represent salient information of the input images are integrated to construct the resulting fused image.
Publication
T. Wan, C. Zhu, Z. Qin, “Multifocus Image Fusion based on Robust Principal Component Analysis“, Pattern Recognition Letters, Volume 34, Issue 9, pages 1001-1008, July 2013.
T. Wan, Z. Qin, C. Zhu, R. Liao, “A Robust Fusion Scheme for Multifocus Images using Sparse Features”, International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, pages 1957-1961, 2013.
Y. Zhang, L. Chen, Z. Zhao, J. Jia, J. Liu, “Multi-focus image fusion based on robust principal component analysis and pulse-coupled neural network”, Optik - International Journal for Light and Electron Optics, May 2014.
Y. Zhang, L. Chen, Z. Zhao, J. Jia, “Multi-focus Image Fusion with Sparse Feature Based Pulse Coupled Neural Network”, Volume 12, No. 2, TELKOMNIKA Telecommunication, Computing, Electronics and Control, 2014.
Y. Zhang, L. Chen, Z. Zhao, J. Jia, “A Novel Pulse Coupled Neural Network based Method for Multi focus Image Fusion”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7, No. 3, pages 361-370, 2014.