An Image Fusion Algorithm Based on Local Laplasse Filter

To deal with the problem of smooth image progressing on the edge, presents a multi scale image fusion algorithm (SLP). The algorithm is based on the local Laplasse filter. First of all, using the local Laplasse filter, the images are decomposed into detail layers and smooth layers. Secondly, the detail layers of the two images are transformed into a fusion rule map by using the sigmoid function, fuse the detail layer according to the map can get the information of the images; Fusing the smooth layers by mean value method can get the picture of the images. Finally, merging the detail layer and smooth layer to generate a clear picture of all the scenes. The algorithm can not only preserve the edge information, but also can reduce the image noise and preserve the image edge details effectively. Experimental results show that the proposed algorithm is superior to Principal Component Analysis(PCA), Ratio pyramid(RP), Morphological pyramid(MP) in terms of performance. Introduction Recent years, image fusion technology has played a huge advantage in many fields. Such as: fusing the CT image and nuclear magnetic resonance MRI image [1] to accurate diagnosis of disease accurately; using the infrared images and visible image [2] to help pilots accurate navigation, etc. With the rapid progress of the information , various image fusion technology will be fully used in different areas, with the development of research and application, the image fusion technology will be more perfect. According to the different stages of image fusion, it can be divided into 3 levels: pixel level (Pixel-level) image fusion, feature level (Feature-level) image fusion and decision level (Decision-level) image fusion. The pixel level fusion algorithm is used in this paper. In Pixel level image fusion, the algorithm analyses the signals of sensors directly after preprocess, the accuracy of image registration affects the fusion effect directly. So, before fusing, the image must be registered. There are mainly two kinds of this fusion algorithm : One based on spatial domain and one based on transform domain. The fusion algorithm based on space is a relatively early approach, it is a simple fusion process that operates on the gray value of the image directly without of the process of transformation and inverse transformation. This fusion algorithm includes principal component analysis(PCA) [3],contrast modulation method [4], Dempster-Shafer(D-S) [5], Markov random field(MRF) [6], Bayesian optimization algorithm (BOA) [7,8], double modal neural network method [9]; The fusion algorithm based on transformation has been developed in recent years, it increases the complexity of computation, but it improves the efficiency of image fusion greatly. The main algorithms are: Laplacian Pyramid(LP) [10], Wavelet transform(WT), Contourlet transform method, etc. This two kinds of fusion algorithm don`t exist independently, and many algorithms combine the two together to achieve the best fusion result. Laplacian Pyramid algorithm In 1983, Burt and Adelson proposed the Laplasse Pyramid algorithm (LPT) [11], and applied it in image decomposition, encoding and reconstruction, etc. The algorithm using the low pass filter layer to hierarchical images, every layers correspond to a specific scale. The larger the scale is, the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) © 2016. The authors Published by Atlantis Press 571