Improved approximate projection transformation image splicing method

Abstract

The invention discloses an improved approximate projection transformation image splicing method, relating to the technical field of image processing. The image splicing method includes S1 image preprocessing, S2 image feature point extraction, and S3 image feature point matching based on an optical flow pyramid. The image processing in S1 includes noise suppression, enhancement of texture and contrast, and normalization of histograms, and further includes calibration of the camera lens, establishment of the corresponding mathematical model based on the cause of image distortion, and correction of contaminated or distorted images by using the calibrated parameters; and the image feature point extraction in S2 adopts a BRISK and / or ORB feature descriptor. The method provided is improved based on the approximate projection transformation (APAP) image splicing method, the pre-processing step is added before the feature extraction step, and the BRISK and / or ORB binary feature descriptor is creatively used in the feature extraction step, and the optical flow pyramid is creatively used in the matching step, so that the image splicing is timesaving and accurate in registering.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to the technical field of image splicing. Background technique [0002] Image stitching technology is the technology of stitching several partially overlapping images into a large seamless high-resolution image. Use ordinary cameras to obtain wide-field scene images. Because the resolution of the camera is fixed, the larger the scene is, the lower the image resolution will be. However, panoramic cameras and wide-angle lenses are not only very expensive, but also have serious distortion. In order to obtain an ultra-wide field of view or even a 360-degree panorama without reducing the image resolution, a computer image stitching method has emerged. [0003] Image stitching technology is mainly divided into three main steps: image preprocessing, image registration, and image fusion. Image preprocessing mainly refers to image noise suppression, texture and contrast enhancement...

AI Technical Summary

Problems solved by technology

However, between images to be registered, there may be multiple nonlinear transformations in different target areas, or there may be large areas without obvious features (such as uniform texture areas or areas of the same color, etc.), which greatly increase the quality of image registration. standard difficulty

[0007] In the prior art, the image mosaic method of Approximate Projective Transformation (APAP) is roughly divided into four steps, namely, feature extraction, feature matching, H matrix calculation, and image fusion. In the feature extraction step, the SIFT feature descriptor is used. The matching speed is slow and the registration is inaccurate in the later matching process

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