Matching Of Multi-exposure Images Based On The Illumination And Scale Invariant Spaces

Image matching,is a process to match two or more images of the same scene taken by different sensors, finding the correspondence between them,and it is an important image analysis and processing technique. Image matching technology is an important research direction in the field of digital image processing ,being widely used such as computer vision, resource analysis, medical image registration, optical and radar tracking, aircraft guided.With the popularity of digital products, digital cameras are gradually becoming a common device in the real life.However, the dynamic range of digital camera is very limited, and only record a range of contrast, brightness and colors, it is difficult to record a comprehensive optical information. When the filming scene dynamic range exceeds the scope of the camera collection, it usually controls the range of brightness information which is achieved by changing the time for exposure. But no matter how to adjust the time, that always still exist overexposed or underexposed areas which leads to some detail losses of highlighting or the darkness department.A group of images with different exposure but same scene can provide more information than a single image. Darker images can provide some details of bright scene, and lighter images can provide the details of the shadow scene. The necessary task is to obtain a series of matching images. In this paper, a method for matching multi-exposure images is introduced. By extracting feature points from the brightness-spaces which are built by multiplying the contrast stretching function with a series of parameters by the input image, we can obtain interesting points robustly even under large illumination, rotation and scale changes. We use the scale invariant feature transform (SIFT) description to describe these points. The experimental results show that the proposed algorithm has good effects on dealing with the matching of multi-exposure images.