基于仿生的S-FREAK水下结构物表面拼接算法
Bionic-based S-FREAK underwater structure surface stitching algo-rithm
俞晓春 1徐晓龙 1方云 1何晓佳 1刘煦阳1
作者信息
- 1. 河海大学信息科学与工程学院,江苏常州 213022
- 折叠
摘要
为更好地了解输水隧洞内壁的实际情况,通常以牺牲分辨率的方式换取水下结构物表面缺陷的全景图像,而较低的分辨率又很难满足监测的需要.针对上述分辨率与全景图像矛盾冲突的问题,提出了一种基于仿生的S-FREAK水下图像拼接算法.考虑到水下图像具有低信噪比、低对比度的特点,算法首先通过模拟水下生物"鲎鱼"的视觉系统,实现了输水隧洞内壁图像的自适应侧抑制增强,突出了图像的架构特征,然后在尺度不变特征变换(scale-invariant feature trans-form,SIFT)的基础上,引入具有人眼视网膜特性的快速视网膜关键点(fast retina keypoint,FREAK)模块,提高了对图像关键特征点的分辨能力,最后结合随机采样一致性(random sample consensus,RANSAC)特征筛选和渐入渐出的融合方法对拼接图像予以修正.实验结果表明,在自适应侧抑制机制的增强下,所提出的方法在增加有效特征点匹配对数的同时,大大提高了拼接的准确度,优化了最终的实现效果.
Abstract
To better understand the interior walls of the water conveyance tunnel,panoramic images of underwater structures'surface defects are obtained at the cost of resolution.However,the lower resolution often falls short of meeting monitoring requirements.To address the conflict between resolution and image acquisition,a bio-inspired S-FREAK underwater image stitching algorithm is proposed.By simulating the vision system of the underwater creature"horseshoe crab,"the algorithm enhances image with adaptive lateral inhibition,highlighting its architectural features,considering the characteristics of low signal-to-noise ratio and low contrast of underwater images.Additionally,the algorithm introduces the fast retina keypoint(FREAK)module,emulating human retina characteristics through scale-invariant feature transform(SIFT),to improve the resolution of key feature points.Finally,random sample consensus(RANSAC)feature filtering and fade in and out fusion methods correct the stitching images.Experimental results show that the enhanced adaptive lateral inhibition mechanism increases the matching logarithm of effective feature points,significantly improves stitching accuracy,and optimizes the final outcome.
关键词
水下缺陷图像/仿生/自适应侧抑制/图像配准/S-FREAK/图像拼接Key words
underwater defect images/bionic/adaptive lateral inhibition/image registration/S-FREAK/image mosaic引用本文复制引用
出版年
2024
NETL
NSTL
万方数据