Research And Evaluation Of Image Stitching Algorithms Based On Improved SIFT

With the development of society and the advancement of digital image technology,people's demand for wide-angle imagery is increasing,which promotes the birth and development of image stitching technology.Since the image information contained in a single image has one-sidedness and limitation,it cannot satisfy the user's demand for wide viewing angle and multi-information.Therefore,how to splicing multiple images with overlapping regions into a image with a wide viewing angle has become a research hotspot in recent years.Image stitching is mainly to obtain more complete image information by expanding the field of view of the image.The stitched image has a wider field of view and contains more image information.And the information in the original image is retained in the stitched image.So the stitched image has better robustness.At present,image stitching technology has been applied to many fields including remote sensing,medical care and virtual reality.It plays an increasingly important role in real life and has a broad application prospect.The image splicing technology mainly fuses two or more images with overlapping regions to generate more comprehensive images,so as to make up for the defect that a single image cannot fully summarize all the detailed information of the target object.The stitched image has more complete image information than the original image.Image stitching technology generally consists of four parts: image acquisition,image preprocessing,image registration and image fusion.The equipment and acquisition methods of image acquisition mainly depend on the application background of image stitching,and the image acquisition equipment and acquisition methods used in different application backgrounds are also different.Image preprocessing can eliminate the interference information existing in the acquired image,ensuring the accuracy and effectiveness of image fusion.The time and accuracy of adjacent images registration are the key factors affecting the image quality and running time generated by image stitching.Image fusion mainly achieves the maximum splicing and merging the information of two or more images to be mosaic to the greatest extent,so that the mosaic and fusion points of different images can achieve smooth transition.Image fusion mainly includes three different fusion levels: pixel-level fusion,feature-level fusion and decision-level fusion.Among them,the pixel-level fusion method is a fusion method currently used in the three fusion levels because it retains more detailed information.Pixel-level fusion mainly includes direct average fusion,weighted average fusion,and gradual-in-gradual-out fusion algorithms.Image registration is the most important position in image stitching technology because it is spatial matching alignment for the stitched image.At present,the common image registration methods mainly include registration methods based on gray information,feature-based registration methods,and transformation domain-based registration methods.In feature-based image registration methods,SIFT algorithm is representative,and is also one of the commonly used registration algorithms in image stitching technology.The feature descriptors generated by the SIFT algorithm are not very sensitive to illumination,scale,rotation,and affine transformation,but they are computationally intensive and time consuming.In this paper,the SIFT algorithm is deeply studied,and it is found that the SIFT algorithm has many problems such as large computation and long time.For the problems existing in the SIFT algorithm,a comparison experiment was carried out on the methods of using the range of 3?3,5?5,7?7 and 9?9 four extreme points.According to the experimental results,this paper proposes a method to reduce the number of feature points in SIFT algorithm.The detection area of local extremum points in SIFT algorithm is expanded and the detection range of 5*5 local extremum points is selected to reduce the number of extremum points.On this basis,this paper further improved the construction method of feature descriptors,using a circular window of 12 rings instead of the square window in the original algorithm,using(3,3,2,2,1,1)The loop is divided,and finally a 78-dimensional feature descriptor is generated.The experimental results show that by improving the feature descriptor construction method,the number of feature points is reduced and the dimension is reduced,which greatly reduces the calculation time of the image registration step.Finally,RANSAC is used to remove the wrong registration point pairs,and the fading in and fade-out fusion algorithm is used to splicing the two images into a more complete image.Because the current objective evaluation of the image stitching effect does not form a unified standard,the global detection can not effectively reflect the detailed information after image stitching.In order to test the splicing effect of the algorithm,this paper proposes an evaluation method based on local region image splicing effect.Local zoning detection is performed on the related indicators of spliced fused images,from information entropy,edge intensity and peak signal to noise ratio.Several indicators were compared with the images before fusion to verify the effect of image stitching.The experimental results show that the image stitched by the algorithm has good visual effects,and the information entropy and other evaluation indexes of the splicing part can be slightly improved compared with the original image.It is proved that the method is fast,reliable and effective under the premise of ensuring good splicing quality.