光电工程
光電工程
광전공정
OPTO-ELECTRONIC ENGINEERING
2014年
7期
75-80
,共6页
李世文%张彬%刘泽民%梁小晓
李世文%張彬%劉澤民%樑小曉
리세문%장빈%류택민%량소효
生物医学光学%光学相干层析技术%波原子变换%图像去噪
生物醫學光學%光學相榦層析技術%波原子變換%圖像去譟
생물의학광학%광학상간층석기술%파원자변환%도상거조
biomedical optical imaging%optical coherence tomography%wave atom transform%image denoising
为了消除或缓解光学相干断层成像方法中散斑等噪声对OCT图像像质退化的影响,提出了基于波原子阈值去噪算法。波原子变换是一种新型的二维多尺度变换,且满足曲线波的抛物比例尺度关系和各向异性征;波原子适用于模式的任意局部方向,能够对轴方向的各向异性模式稀疏展开。本文利用波原子阈值去噪算法,对人眼眼底组织和手指指尖皮肤的OCT图像进行降噪处理,并与传统的小波阈值算法和快速曲波算法对OCT样品图像去噪效果进行对比分析。结果表明,基于波原子阈值去噪方法能够有效地抑制OCT图像散斑噪声,并能保持图像边缘细节特征。
為瞭消除或緩解光學相榦斷層成像方法中散斑等譟聲對OCT圖像像質退化的影響,提齣瞭基于波原子閾值去譟算法。波原子變換是一種新型的二維多呎度變換,且滿足麯線波的拋物比例呎度關繫和各嚮異性徵;波原子適用于模式的任意跼部方嚮,能夠對軸方嚮的各嚮異性模式稀疏展開。本文利用波原子閾值去譟算法,對人眼眼底組織和手指指尖皮膚的OCT圖像進行降譟處理,併與傳統的小波閾值算法和快速麯波算法對OCT樣品圖像去譟效果進行對比分析。結果錶明,基于波原子閾值去譟方法能夠有效地抑製OCT圖像散斑譟聲,併能保持圖像邊緣細節特徵。
위료소제혹완해광학상간단층성상방법중산반등조성대OCT도상상질퇴화적영향,제출료기우파원자역치거조산법。파원자변환시일충신형적이유다척도변환,차만족곡선파적포물비례척도관계화각향이성정;파원자괄용우모식적임의국부방향,능구대축방향적각향이성모식희소전개。본문이용파원자역치거조산법,대인안안저조직화수지지첨피부적OCT도상진행강조처리,병여전통적소파역치산법화쾌속곡파산법대OCT양품도상거조효과진행대비분석。결과표명,기우파원자역치거조방법능구유효지억제OCT도상산반조성,병능보지도상변연세절특정。
In order to eliminate or suppress the noise (especially speckle noise) in frequency domain optical coherence tomography (FD-OCT) imaging system which will degrade the images quality, a speckle denoise method based on wave atom threshold de-noising algorithm is presented. Wave atom transform is a novel two-dimensional multi-scale transform, and it meets the curve-wave parabolic proportion scale relationship and anisotropy. Moreover, wave atom is suit to mode at any of the local direction to the axial direction of the anisotropic with model sparse expand. FD-OCT images of the human eye fundus tissue and the skin of finger tip are taken as test image samples. The comparative studies with the proposed method, traditional wavelet threshold algorithm and fast curvelet transform algorithm are given. The results show that the wave atom-based threshold de-noising method can successfully despeckle the noise in OCT images but maintain the detail edges of testing images. It is shown that the proposed method is better than the traditional wavelet threshold method and the fast curvelet transform.