Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching ...Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.展开更多
Image enhancement plays an important role in many applications of medical imaging. Image enhancement technologies can improve the qualities of medical images with low contrast and high level noise by stretching contra...Image enhancement plays an important role in many applications of medical imaging. Image enhancement technologies can improve the qualities of medical images with low contrast and high level noise by stretching contrast, suppressing noise and so on. Such images processed by image enhancement technologies are helpful to doctors in analyses and diagnoses. In this paper, we present a technical review of various existing image enhancement methodologies which are often emoloved.展开更多
基金Supported by the National Natural Science Foundation of China(No.61771186)the Heilongjiang Provincial Natural Science Foundation of China(No.YQ2020F012)the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(No.UNPYSCT-2017125).
文摘Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.
基金supported by the National Research Foundation for the Doctoral Program of Higher Education of China (20110131130004)Independent Innovation Foundation of Shandong University,IIFSDU (2012TB013)Ji’nan Science and Technology Development Project (No.201202015)
文摘Image enhancement plays an important role in many applications of medical imaging. Image enhancement technologies can improve the qualities of medical images with low contrast and high level noise by stretching contrast, suppressing noise and so on. Such images processed by image enhancement technologies are helpful to doctors in analyses and diagnoses. In this paper, we present a technical review of various existing image enhancement methodologies which are often emoloved.
