红外技术
紅外技術
홍외기술
INFRARED TECHNOLOGY
2013年
1期
27-30
,共4页
陆凯%李成金%赵勋杰%邹薇%张雪松
陸凱%李成金%趙勛傑%鄒薇%張雪鬆
륙개%리성금%조훈걸%추미%장설송
亚像素图像%配准算法%角点预检测%边缘检测%Harris 角点
亞像素圖像%配準算法%角點預檢測%邊緣檢測%Harris 角點
아상소도상%배준산법%각점예검측%변연검측%Harris 각점
sub-pixel image%registration algorithm%corner preliminary detection%edge detection%Harris corner
图像超分辨率重建是在现有红外探测器基础上提升空间分辨率的一种有效方法.超分辨率图像重建是利用一组相互之间存在亚像素位移的低分辨率图像构造出一幅高分辨率的图像,快速、高精度估计图像间的位移是其关键技术之一.提出了一种用于超分辨率重建的亚像素配准算法,算法由特征检测、像素级配准和亚像素级配准三个处理过程组成.在特征检测过程,首先采用梯度算子对图像进行边缘检测,然后对边缘点进行角点预检测,排除非角点像素点,之后再进行 Harris 角点检测,大大减少了计算量;在像素级配准过程,用 NCC 算法进行像素级配准,用统计方法去除误匹配点对;在亚像素级配准过程,先对像素级匹配点的邻域进行插值放大,再进行亚像素匹配,误匹配点剔除,相对偏移量计算.对提出的算法进行了仿真实验,结果显示本算法的速度较类似算法速度有较大的提高.
圖像超分辨率重建是在現有紅外探測器基礎上提升空間分辨率的一種有效方法.超分辨率圖像重建是利用一組相互之間存在亞像素位移的低分辨率圖像構造齣一幅高分辨率的圖像,快速、高精度估計圖像間的位移是其關鍵技術之一.提齣瞭一種用于超分辨率重建的亞像素配準算法,算法由特徵檢測、像素級配準和亞像素級配準三箇處理過程組成.在特徵檢測過程,首先採用梯度算子對圖像進行邊緣檢測,然後對邊緣點進行角點預檢測,排除非角點像素點,之後再進行 Harris 角點檢測,大大減少瞭計算量;在像素級配準過程,用 NCC 算法進行像素級配準,用統計方法去除誤匹配點對;在亞像素級配準過程,先對像素級匹配點的鄰域進行插值放大,再進行亞像素匹配,誤匹配點剔除,相對偏移量計算.對提齣的算法進行瞭倣真實驗,結果顯示本算法的速度較類似算法速度有較大的提高.
도상초분변솔중건시재현유홍외탐측기기출상제승공간분변솔적일충유효방법.초분변솔도상중건시이용일조상호지간존재아상소위이적저분변솔도상구조출일폭고분변솔적도상,쾌속、고정도고계도상간적위이시기관건기술지일.제출료일충용우초분변솔중건적아상소배준산법,산법유특정검측、상소급배준화아상소급배준삼개처리과정조성.재특정검측과정,수선채용제도산자대도상진행변연검측,연후대변연점진행각점예검측,배제비각점상소점,지후재진행 Harris 각점검측,대대감소료계산량;재상소급배준과정,용 NCC 산법진행상소급배준,용통계방법거제오필배점대;재아상소급배준과정,선대상소급필배점적린역진행삽치방대,재진행아상소필배,오필배점척제,상대편이량계산.대제출적산법진행료방진실험,결과현시본산법적속도교유사산법속도유교대적제고.
The image super-resolution (SR) reconstruction is an effective method to enhance spatial resolution on the basis of existing infrared detectors. Image SR reconstruction is a process that obtains a high resolution image from a set of sub-pixel shifted low-resolution (LR) images. A rapid and high accuracy algorithm for estimating the shift between the images is one of the key technologies. In this paper, a fast sub-pixel registration algorithm is putted forward. The algorithm consists of three processes: feature detection, pixel-level registration and sub-pixel level registration. In the feature detection process, Firstly, we extract edges from the original images by Sobel operator. Secondly, preliminary detection of corner is used to remove the non-corner pixels. And finally, the Harris corners are extracted. In this way, the time for corner detecting is reduced greatly. In the pixel level registration process, we use NCC algorithm for registration, then statistical method is applied to remove false matching points. In the sub-pixel level registration process, we carry out interpolation in the neighborhood of the pixel-level match points, then the NCC matching and statistical method are used once again. Simulation experiment of the proposed algorithm is carried out, and the result shows that the speed of the proposed algorithm is significantly faster than the similar algorithms.