光电工程, 2019, 46 (1): 180084, 网络出版: 2019-01-18  

基于张量分解和卷积稀疏表示的多曝光图像融合

Multi-exposure image fusion based on tensor decomposition and convolution sparse representation
戚余斌 1郁梅 1,2,*姜浩 1,3邵华 1蒋刚毅 1,2
作者单位
1 宁波大学信息科学与工程学院, 浙江 宁波 315211
2 南京大学计算机软件新技术国家重点实验室, 江苏 南京 210093
3 浙江工商职业技术学院智能家电宁波市重点实验室, 浙江 宁波 315012
摘要
针对多曝光图像融合中存在细节丢失和颜色失真等问题, 本文提出了一种基于张量分解和卷积稀疏表示的多曝光图像融合方法。张量分解作为一种对高维数据低秩逼近的方式, 在多曝光图像特征提取方面有较大的潜力, 而卷积稀疏表示是对整幅图像进行稀疏优化, 能最大程度地保留图像的细节信息。同时, 为了避免融合图像出现颜色失真, 本文采取亮度与色度分别融合的方式。首先通过张量分解得到源图像的核心张量; 然后在包含信息最多的第一子带上提取边缘特征; 接着对边缘特征图进行卷积稀疏分解, 继而利用分解系数的L1范数来得到每个像素的活跃水平; 最后用“赢者取全”策略生成权重图, 从而加权得到融合后的亮度分量。与亮度融合不同的是, 色度分量则采用简单的高斯加权方式进行融合, 在一定程度上解决了融合图像的颜色失真问题。实验结果表明, 所提出的方法具有良好的细节保留能力。
Abstract
In view of the problem about the loss of detail and color distortion in multi-exposure image fusion, this paper proposed a multi-exposure image fusion method based on tensor decomposition and convolution sparse representation. Tensor decomposition, as an approach of low-rank approximation for high-dimensional data, has great potential in feature extraction of multi-exposure images. Convolution sparse representation is a sparse optimization method for the whole image, which can preserve the detail information of the image to the greatest extent. At the same time, in order to avoid color distortion in the fused image, this paper adopted the method of separately fusing luminance and chrominance. Firstly, the core tensor of the source image was obtained by tensor decomposition. Besides, edge features were extracted on the first sub-band which contains the most information. Then the edge feature map was sparsely decomposed to obtain the activity level of each pixel by using L1 norm of the decomposition coefficient. Finally, take "winner-take-all" strategy to generate weight map so as to obtain the fused luminance components. Unlike the process of luminance fusion, chrominance components were fused by simple Gaussian weighting method, which solves the color distortion problem for the fused image to a certain extent. The experimental results show that the proposed method has great detail preservation ability.

戚余斌, 郁梅, 姜浩, 邵华, 蒋刚毅. 基于张量分解和卷积稀疏表示的多曝光图像融合[J]. 光电工程, 2019, 46(1): 180084. Qi Yubin, Yu Mei, Jiang Hao, Shao Hua, Jiang Gangyi. Multi-exposure image fusion based on tensor decomposition and convolution sparse representation[J]. Opto-Electronic Engineering, 2019, 46(1): 180084.

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