Journal
OPTICAL ENGINEERING
Volume 52, Issue 1, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.OE.52.1.017001
Keywords
image fusion; nonsubsampled shearlet transform; pulse-coupled neural network; time matrix
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Funding
- National Natural Science Foundation of China [61072109, 61272011]
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A new technique for image fusion based on nonsubsampled shearlet transform (NSST) and improved pulse-coupled neural network (PCNN) is proposed. NSST, as a novel multiscale geometric analysis tool, can be optimally efficient in representing images and capturing the geometric features of multidimensional data. As a result, NSST is introduced into the area of image fusion to complete the decompositions of source images in any scale and any direction. Then the basic PCNN model is improved to be improved PCNN (IPCNN), which is more concise and more effective. IPCNN adopts the contrast of each pixel in images as the linking strength beta, and the time matrix T of subimages can be obtained via the synchronous pulse-burst property. By using IPCNN, the fused subimages can be achieved. Finally, the final fused image can be obtained by using inverse NSST. The numerical experiments demonstrate that the new technique presented in this paper is competitive in the field of image fusion in terms of both fusion performance and computational efficiency. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.52.1.017001]
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