一种基于差异系数的稀疏度自适应图像去噪算法

一种基于差异系数的稀疏度自适应图像去噪算法
焦莉娟;王文剑
【期刊名称】《计算机科学》
【年(卷),期】2018(045)002
【摘要】With the remarkable adaptability and the details recovery capability,K-SVD is a highly effective method based on sparse representation theory in image denoising.But the sparsity K should be given in advance,and different images have different K values in fact.On the other hand,pursuit algorithms which are used in evaluating the relevance between vectors of an image by calculating vector inner product,are brought into K-SVD to train sparse coefficients.Denoising effect is reduced because a few noisy pixels are likely to cause false relevance.This paper addressed this problem and proposed a novel sparsity-adaptive speeded K-SVD(SASK-SVD) algorithm based on different coefficient,which can improve the efficiency.The different coefficient is to eliminate false relevance.The sparsity K is adaptively generated by using the average correlation as the threshold.This study conducted extensive experiments to demonstrate these ideas.The experimental results show that the proposed method achieves the state-of-the-art denoising performance.%基于压缩感知的K-means Singular Value Decomposition (K-SVD)图像去噪算法具有良好的自适应性和细节恢复能力,但需事先给定稀疏度K.该方法的去噪效果会受到图像稀疏度的影响.另外,训练初始系数时用到的追踪类算法
中通过向量内积值的大小评定图像分量间相关度的方法,因存在大值噪声点,容易造成假相关,从而影响去噪效果.提出基于差异系数的稀疏度自适应K-SVD去噪算法,通过引入差异系数来平衡因噪声点造成的假相关问题,同时使用相关度均值作为阈值来自适应地产生稀疏度K,避免因给定不恰当的稀疏度而影响去噪效果的问题.在USC标准库上的实验结果表明,所提算法在去噪效果方面有一定的优越性.
【总页数】5页(P94-97,134)
【作者】焦莉娟;王文剑
【作者单位】忻州师范学院计算机系山西忻州034000;山西大学计算机与信息技术学院太原030006
【正文语种】中文
【中图分类】TP391
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