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Digital watermarking algorithm based on scale-invariant feature regions in non-subsampled contourlet transform domain 被引量:8
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作者 Jian Zhao Na Zhang +1 位作者 Jian Jia Huanwei Wang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第6期1310-1315,共6页
Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform (NSCT) domain is proposed. The largest energy... Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform (NSCT) domain is proposed. The largest energy sub-band after NSCT is selected to embed watermark. The watermark is embedded into scaleinvariant feature transform (SIFT) regions. During embedding, the initial region is divided into some cirque sub-regions with the same area, and each watermark bit is embedded into one sub-region. Extensive simulation results and comparisons show that the algorithm gets a good trade-off of invisibility, robustness and capacity, thus obtaining good quality of the image while being able to effectively resist common image processing, and geometric and combo attacks, and normalized similarity is almost all reached. 展开更多
关键词 multi-scale geometric analysis (MGA) non-subsampled contourlet transform (nsct scale-invariant featureregion.
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Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain 被引量:3
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作者 Periyavattam Shanmugam Gomathi Bhuvanesh Kalaavathi 《Circuits and Systems》 2016年第8期1598-1610,共13页
Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an impor... Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, Q<sup>AB/F</sup> as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, Q<sup>AB/F</sup>is 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images. 展开更多
关键词 Image Fusion non-subsampled Contourlet Transform (nsct) Medical Imaging Fusion Rules
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基于多尺度Retinex和NSCT的泡沫图像增强方法 被引量:3
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作者 李建奇 阳春华 +1 位作者 朱红求 曹斌芳 《高技术通讯》 CAS CSCD 北大核心 2013年第2期160-166,共7页
针对矿物浮选过程中获取的泡沫图像易受环境光照影响、噪声干扰和存在灰度对比度低等问题,提出一种结合多尺度Retinex(MSR)算法和非下采样Contoudet变换(NSCT)的泡沫图像增强方法。该方法首先针对光照使得浮选泡沫图像存在亮度不均,用... 针对矿物浮选过程中获取的泡沫图像易受环境光照影响、噪声干扰和存在灰度对比度低等问题,提出一种结合多尺度Retinex(MSR)算法和非下采样Contoudet变换(NSCT)的泡沫图像增强方法。该方法首先针对光照使得浮选泡沫图像存在亮度不均,用一种区域自适应分割的MSR算法,通过调整权值改善图像的整体亮度均匀性;然后采用NSCT,通过构造分类函数完成对包含细节和噪声的高频系数处理,有效地弥补了Retinex算法在细节增强效果和噪声消除方面的不足。实验仿真结果表明,该方法能有效增强泡沫图像的轮廓、边缘和细节,抑制噪声,明显改善泡沫图像的视觉效果,为浮选泡沫图像的特征提取和品位分析奠定基础。 展开更多
关键词 泡沫图像 图像增强 多尺度Retinex(MSR) 非下采样Contourlet变换(nsct)
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基于多层次多方向分解的医学图像融合算法 被引量:14
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作者 宋瑞霞 王孟 +1 位作者 王小春 余建德 《计算机工程》 CAS CSCD 北大核心 2017年第10期179-185,共7页
传统多模态医学图像融合技术融合后图像的细节表达不清晰、病灶不明显。为此,设计一种V-变换与非下采样Contourlet变换(NSCT)相结合的融合方法。对源图像进行多层次V-分解,使其被分解为轮廓图像和细节图像两部分,对其中的轮廓图像做NSC... 传统多模态医学图像融合技术融合后图像的细节表达不清晰、病灶不明显。为此,设计一种V-变换与非下采样Contourlet变换(NSCT)相结合的融合方法。对源图像进行多层次V-分解,使其被分解为轮廓图像和细节图像两部分,对其中的轮廓图像做NSCT变换,在NSCT域中设计融合方案,针对细节图像给出细节信息的融合策略,将融合后的轮廓图像和细节图像叠加,以得到最终融合图像。实验结果表明,与传统离散小波变换、NSCT变换的方法相比,该算法在视觉效果和评价指标方面都有较好的表现。 展开更多
关键词 图像融合 医学图像 V-系统 多层次V分解 非下采样CONTOURLET变换
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