Research On Image Fusion Combined With Image Segmentation

Image fusion, as an important branch of image engineering for decades has undergone a profound technological change, image fusion rules and fusion gradually moving to the human brain’s cognitive function and visual perception system simulation imitation, image Fusion is gradually to the intelligent, understanding abstract visual direction. Image fusion refers to the multi-channel source image data collected on the same target by the image processing and computer technology, to maximize the extraction of beneficial information of each channel, and finally integrated into the process of high-quality images. This process improves the utilization of image information, to improve the accuracy and reliability of computer interpretation, improve spatial resolution and spectral resolution of the original image, which will help to monitor. Infrared and visible image fusion image fusion has been a hot research in military reconnaissance, security monitoring, and other fields have a wide range of needs and applications.This paper focuses on the pixel-level infrared and visible image fusion method, the focus is the study of infrared and visible image fusion algorithms and image segmentation. This article describes the significance of the research background and current situation of image fusion technology research at home and abroad; a detailed analysis of various fusion algorithms applied to image fusion, multi-resolution analysis principles elaborated, including the Laplace transform, discrete wavelet transform,non-under sampling Contourlet transform, stationary wavelet transform, and analyze their strengths and weaknesses.In the traditional structure based on multi-resolution analysis Piella fusion algorithm on the basis of research, a new fusion method, which combines image fusion method of multi-resolution image segmentation analysis. the first use of two-dimensional histogram entropy method for infrared image segmentation, and then take advantage of multi-resolution analysis of the background infrared and visible light images fused into the background image, and finally infrared target image and the background image fusion to get the final fused image. Experimental results show that compared with traditional fusion methods, both in subjective or objective evaluation indicators have a more significant improvement in the method.On this basis, the paper also introduces the fuzzy C-means clustering methods, theuse of two-dimensional histogram entropy fuzzy C-means clustering method for infrared image segmentation, and apply multi-resolution image fusion analysis. The results of the method of infrared target surface to get a more complete segmentation,background point mistakenly introduced compared to the traditional segmentation methods have some improvement. Target information fusion results most comprehensive background information and infrared images of visible images, with good results.