红外与毫米波学报
紅外與毫米波學報
홍외여호미파학보
JOURNAL OF INFRARED AND MILLIMETER WAVES
2009年
6期
440-444
,共5页
汪大宝%刘上乾%寇小明%洪鸣
汪大寶%劉上乾%寇小明%洪鳴
왕대보%류상건%구소명%홍명
背景杂波抑制%红外弱小目标%马尔可夫随机场%正则化%自适应滤波
揹景雜波抑製%紅外弱小目標%馬爾可伕隨機場%正則化%自適應濾波
배경잡파억제%홍외약소목표%마이가부수궤장%정칙화%자괄응려파
background clutter suppression%infrared dim small target%Markov random field(MRF)%regularization%adaptive filtering
针对复杂背景下红外弱小目标检测难题,将背景杂波抑制归结为从原始红外弱小目标图像中重建目标数据的过程,据此提出了一种基于马尔可夫随机场模型(MRF)的自适应正则化滤波算法.该算法采用MRF,建立了红外弱小目标图像的先验概率模型,并根据图像的粗糙度设计了新的势函数.在此基础上,采用MRF对背景杂波抑制过程进行正则化处理,从而实现了对红外背景杂波的自适应各向异性抑制.理论分析与实验结果表明,该算法能够随图像局部纹理特征的变化自适应地调整滤波算子结构,从而可在复杂背景下自适应地抑制杂波、增强信号,有效地提高了图像的信噪比,且该算法结构简单,更易于硬件实时实现.
針對複雜揹景下紅外弱小目標檢測難題,將揹景雜波抑製歸結為從原始紅外弱小目標圖像中重建目標數據的過程,據此提齣瞭一種基于馬爾可伕隨機場模型(MRF)的自適應正則化濾波算法.該算法採用MRF,建立瞭紅外弱小目標圖像的先驗概率模型,併根據圖像的粗糙度設計瞭新的勢函數.在此基礎上,採用MRF對揹景雜波抑製過程進行正則化處理,從而實現瞭對紅外揹景雜波的自適應各嚮異性抑製.理論分析與實驗結果錶明,該算法能夠隨圖像跼部紋理特徵的變化自適應地調整濾波算子結構,從而可在複雜揹景下自適應地抑製雜波、增彊信號,有效地提高瞭圖像的信譟比,且該算法結構簡單,更易于硬件實時實現.
침대복잡배경하홍외약소목표검측난제,장배경잡파억제귀결위종원시홍외약소목표도상중중건목표수거적과정,거차제출료일충기우마이가부수궤장모형(MRF)적자괄응정칙화려파산법.해산법채용MRF,건립료홍외약소목표도상적선험개솔모형,병근거도상적조조도설계료신적세함수.재차기출상,채용MRF대배경잡파억제과정진행정칙화처리,종이실현료대홍외배경잡파적자괄응각향이성억제.이론분석여실험결과표명,해산법능구수도상국부문리특정적변화자괄응지조정려파산자결구,종이가재복잡배경하자괄응지억제잡파、증강신호,유효지제고료도상적신조비,차해산법결구간단,경역우경건실시실현.
Aiming at the difficulty in detecting infrared(IR) dim small target under strong background clutter, the process of background suppression was attributed to the reconstruction of the target signal from the original IR dim small target image. Thus, a novel adaptive regularization filtering algorithm based on Markov random field(MRF) model was proposed. In our algorithm, the prior probability model of the IR dim small image was established by MRF, and a new potential function was introduced according to the roughness of the IR image. On this basis, the adaptive anisotropic filtering effect for background clutter suppression was realized by regularizing the process of background clutter suppression with MRF. Theoretical analysis and experimental results show that this algorithm can adjust the operator adaptively according to the local texture distribution character of the image. Thus, the target was enhanced and strong background clutter was eliminated. The proposed algorithm can improve the signal-to-noise ratio(SNR) of the image obviously with the advantage of its logical structure simple to be implemented in real-time system.