| 基于CHMM 的尖锐频率局部化Contourlet 域图像去噪 |
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| 引用本文： | 张红英,罗晓清,吴小俊.基于CHMM 的尖锐频率局部化Contourlet 域图像去噪[J].红外与激光工程,2014,43(7):2341-2348. |
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| 作者姓名： | 张红英 罗晓清 吴小俊 |
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| 作者单位： | 1.江南大学物联网工程学院,江苏无锡 214122 |
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| 基金项目： | 国家自然科学基金(60973094,61103128);教育部科技研究重大项目(311024);新进科研人员启动经费(1255210232110940);111高等学校学科创新引智计划(B12018) |
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| 摘 要： | 提出了一种基于上下文信息隐马尔科夫模型（CHMM）的尖锐频率局部化Contourlet 域图像去噪方法。首先，对噪声图像进行循环平移操作，再利用尖锐频率局部化Contourlet 变换对平移后的图像进行分解，解决了原始Contourlet 变换频率非局部化及缺乏平移不变性的问题，抑制图像在奇异点处产生的伪吉布斯现象。然后，设计一种新的上下文构造方案，针对图像高频子带系数构建CHMM 进行去噪处理。最后，执行尖锐频率局部化Contourlet 逆变换以及逆向循环平移操作获得最终的去噪图像。文中方法采用有效的变换机制并利用上下文信息构建了一个全面的统计相关模型，充分表达了轮廓波高频子带系数在尺度间的持续性、尺度内的多方向选择性和空间邻域内的能量聚集特性，更加有利于图像的去噪处理。实验结果表明:该方法在提高去噪图像PSNR 值的同时进一步改善了其视觉效果，去噪性能优于基于小波变换和原始Contourlet 变换的去噪方法。
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| 关 键 词： | 上下文 尖锐频率 图像去噪 隐马尔科夫模型 循环平移 |
| 收稿时间： | 2013-11-14 |
Contextual hidden Markov model-based image denoising in sharp frequency localized Contourlet transform domain |
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| Affiliation: | 1.School of IoT Engineering,Jiangnan University,Wuxi 214122,China |
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| Abstract: | A contextual hidden Markov model (CHMM) for image denoising application was presented in sharp frequency localized Contourlet transform (SFLCT) domain. Firstly, cycle spinning technology was employed on the noisy image, and then decomposed by the SFLCT into sub-images, solving one drawback of the original Contourlet transform that its basis images were not localized in the frequency domain and compensating for the lack of translation invariance property of SFLCT, suppressing the pseudo-Gibbs phenomena around singularities of images. Secondly, a new context design scheme was proposed, CHMM was established aiming at high frequency subband coefficients and applied to image denoising. Finally, the denoised image was reconstructed by inverse SFLCT and inverse cycle spinning operation. Valid transform mechanism and a comprehensive statistical correlative model that was constructed by integrating context information with HMM were utilized, which could fully express persistence across scales, directional selectivity within scales and energy concentration in the space neighborhood of Contourlet coefficients, consequently, the proposed method was more effective and suitable for image denoising. Some experiments are conducted to verify the method is more potential and certainly superior to wavelet transform method and the original Contourlet transform method, both in subjective evaluation and numerical guidelines. |
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