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基于改进SURF的低照度图像拼接方法

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低照度图像拼接是一种在光照不足条件下将不同视角图像拼接成一幅大视场图像的技术.光照不足带来的图像对比度低、噪声高等问题,导致特征提取鲁棒性差、数量少,从而难以进行特征匹配和图像拼接.对此,提出了一种基于改进加速鲁棒特征(SURF)的低照度图像拼接方法.该方法首先通过低照度图像的积分图构造尺度空间并进行Laplacian运算,执行边缘提取和二值化处理算法;然后根据边缘提取图像和二值化图像计算阴影区域中的边缘(ESR)图像,用于获取尺度权重,从而动态调整SURF特征提取阈值,有效解决特征点像素阈值与图像整体亮度不匹配导致特征提取算法鲁棒性差的问题;同时,尺度权重也可作为多尺度Retinex算法中的加权系数,进而优化图像增强效果;此外,采用二进制描述符来加速特征描述与特征匹配过程,最后通过匹配关系计算单应矩阵,对增强后的图像进行单应变换和拼接.实验结果表明,本文算法显著提高了低照度环境下图像拼接的速度和效率,相比于传统SURF算法具有更好的鲁棒性和自适应性.
Low-Light Image Stitching Method Based on Improved SURF
Low-light image stitching is a technique that enables the stitching of images taken from different perspectives into a large field-of-view image under insufficient lighting conditions.The low contrast and high noise of images caused by inadequate lighting compromise the robustness and quantity of feature extraction,making feature matching and image stitching challenging.In response,this study proposes a low-light image stitching method based on an improved speeded-up robust feature(SURF)algorithm.In this method,a scale space was constructed first using the integral image of low-light images and Laplacian operations were performed,followed by edge extraction and binarization of the images.Further,the edges-in-shaded-region(ESR)image was generated based on the edge-extracted and binarized images to obtain scale weights,thereby dynamically adjusting the SURF feature extraction threshold.This effectively resolves the issue of mismatch between feature point pixel thresholds and overall image brightness,enhancing the robustness of the feature extraction algorithm.Additionally,the obtained scale weights can serve as weighting coefficients for the multiscale Retinex algorithm to achieve better image enhancement effects.In this method,binary descriptors were employed to accelerate the feature description and matching process.Finally,a homography matrix was calculated based on matching relationships to perform homography transformation and stitching of the enhanced images.Experimental results demonstrate that the proposed algorithm effectively improves the speed and performance of low-light image stitching,offering better robustness and adaptability compared with the traditional SURF algorithm.

image processinglow-light imageimage stitchingimage enhancementspeed-up robust feature algorithm

姬谕、丁朋、刘楠、茹占强、李振曜、程素珍、王争光、龚精武、殷志珍、吴菲、宋贺伦

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中国科学技术大学纳米技术与纳米仿生学院,安徽 合肥 230026

中国科学院苏州纳米技术与纳米仿生研究所,江苏 苏州 215123

图像处理 低照度图像 图像拼接 图像增强 加速鲁棒特征算法

江苏省产业前瞻与关键核心技术-碳达峰碳中和科技创新专项江苏省"六大人才高峰"高层次人才项目苏州市碳达峰碳中和科技支撑重点专项苏州市新型激光显示技术重点实验室

BE2022021-1XYDXX-211ST202219SZS2022007

2024

10.3788/LOP240470

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子：1.153
ISSN：1006-4125
年,卷(期)：2024.61(18)
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