欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对脉冲耦合神经网络（PCNN）图像融合模型对含噪声图像敏感和融合时间效率不高的问题，通过引入压缩感知（CS）技术对传统模型进行改造，提出了一种融入CS技术的新型快速脉冲耦合图像融合方法，不仅能够弥补传统脉冲耦合模型抗噪声能力不强的缺陷，还可以实现含噪声图像去噪和图像融合同步进行，有效克服了传统去噪融合方法中人为将去噪过程和融合过程分开而造成的信息不一致等问题，在一定程度上提高了融合效果和时间效率。在多聚焦图像和小目标图像上进行了相关实验研究，并在视觉效果和性能评价、含噪声多少与方法性能、稳定性等方面进行了详细分析。实验结果表明，新方法无论从融合效果还是评价指标上均较一些相关方法显示出一定的优越性。
英文摘要：
Compressed sensing （CS） has attracted a lot of attention in recent years,and it remains a hot research topic in pattern recognition and image processing. In this paper,a novel method by integrating compressed sensing with pulse coupled neural networks （PCNN） is put forward for the purpose of overcoming the drawback of noise sensitivity and poor time efficiency of the classical PCNN. The proposed method not only has good ability to overcome the noise, but also performs the denoising and image fusion simultaneously, whereas denoising and fusion processes are carried out separately for many conventional image fusion approaches and this would result in information inconsistency. Nevertheless, by integrating the merits of CS and PCNN, the proposed method can greatly improve the image fusion efficiency and reduce the computation time to some extent. Extensive experiments are carried out on the multi-focus images and small-target images. In addition,we detailedly analyze from the respects of fusion performance, noise level,fusion efficiency, algorithmic stability and so on. And experimental results indicate that the proposed method outperforms some existing image fusion methods in terms of both the visual quality and a variety of quantitative evaluation criteria.
同期刊论文项目
同项目期刊论文
An optimal symmetrical null space criterion of Fisher discriminant for feature extraction and recogn
期刊信息
位置：