不同光照条件下的图像拼接技术研究

发布时间：2018-03-27 05:10

本文选题：光照处理　切入点：图像拼接　出处：《沈阳工业大学》2017年硕士论文

【摘要】：图像拼接技术被广泛应用于虚拟现实、遥感技术、医学等各个领域,而不同的光照条件对于自然图像也是必不可少的影响因素,因此不同光照条件下的图像拼接技术的研究具有重要学术意义与应用价值。图像拼接是一种将具有重叠区域的多张场景图像拼接融合成一张高分辨率的全景图像技术,其技术的关键在于精准地匹配与融合待拼接图像而使所得结果图像没有拼接迹象。而对于不同光照的图像预处理方法则对于图像拼接效果起到至关重要的作用,本文对光照不一致情况下的待拼接图像处理方法和拼接方法进行深入研究以保证较好图像拼接效果。本文提出了两种图像光照处理方法对待拼接图像进行光照处理。其中逐像素正交分解法提取图像彩色光照不变信息,去除光照干扰同时保留图像的彩色信息进行拼接。第二种光照处理方法是基于深度学习框架生成光照恒定图像,利用深度卷积神经网络结构提取光照信息,通过数据训练模型学习得出平均光照,使待拼接图像达到光照一致,进而进行下一步的图像拼接操作。最后对两种光照处理算法结果进行对比,并总结各自的特点与优势。在图像拼接阶段,本文采用重叠区域匹配验证方法对图像误匹配对进行排除,并通过圆柱投影变换对图像进行配准拼接,保留图像原有几何信息。本文选用波特兰州立大学的APAP图像拼接数据库进行图像拼接实验,其中深度学习处理光照部分在Ubuntu系统下基于CAFFE网络框架完成。实验证明本文所提方法对于不同光照条件下的图像拼接取得较好效果。
[Abstract]:Image stitching technology is widely used in virtual reality, remote sensing, medical and other fields, and the effects of different illumination conditions is essential for natural image factors, so according to different research of image stitching technology under the condition of light has important academic significance and application value. Image mosaic is a fusion of the overlapping region a plurality of scene images together into a high resolution panoramic image technology, its key technique is accurate, and fusion images and the results of image stitching and no signs. For different illumination image preprocessing method is a crucial role for image mosaic effect, the light is not consistent to be in-depth studies to ensure better image mosaic method and image mosaic mosaic method under the condition of processing. This paper proposes two kinds of image illumination at Methods the mosaicked image illumination. The pixel color image of orthogonal illumination invariant information decomposition method to remove the light color information of interference while preserving the image mosaic. Second kinds of light treatment method is deep learning framework to generate constant illumination image based on the depth structure of convolutional neural network extraction of light the information obtained by the data, the average light training model of learning, to achieve image mosaic illumination consistency, then the image splicing operation next. At the end of the two light processing algorithm are compared and summed up their own characteristics and advantages. In the stage of image mosaic, overlap area matching verification method to eliminate the image matching error, and through the cylindrical projection transform of image registration, retain original image geometric information. In this paper, the State University of Portland APA P image mosaic database is used for image mosaic experiment. Deep learning processing and illumination part is completed under Ubuntu system based on CAFFE network framework. Experiments show that the proposed method achieves good results for image mosaic under different illumination conditions.

【学位授予单位】：沈阳工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41

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