以提升模糊舰船图像视觉传达效果，获取更多的舰船图像内部信息为目的，研究基于视觉传达的模糊舰船图像优化方法。采用基于视觉传达的模糊舰船图像去模糊处理方法，根据模糊舰船图像模型，分别确定彩色模糊舰船图像3个RGB 色彩通道的模糊核，并将确定的模糊核应用到基于细稀疏表示的去模糊模型中，对舰船图像去模糊处理。依照人类视觉对色彩度的具体感知水平，采用六角椎体模型对去模糊化后的舰船图像进行视觉传达效果优化，重建去模糊后舰船图像颜色模型空间内的明度参数。实验结果显示，所研究方法能够有效实现舰船图像去模糊处理，处理后图像与清晰舰船图像相比结构相似度均达到0.89以上，信息熵值均在9.1以上。
In order to improve the visual communication effect of fuzzy ship image and obtain more internal information of ship image, the optimization method of fuzzy ship image based on visual communication is studied. The fuzzy ship image deblurring method based on visual communication is adopted. Based on the fuzzy ship image model, the fuzzy cores of the three RGB color channels of the color fuzzy ship image are determined respectively, and the determined fuzzy cores are applied to the deblurring model based on thin sparse representation to deblurch the ship image. According to the specific perception level of human vision on chromaticity, the hexagonal cone model is used to optimize the visual communication effect of the deblurred ship image, and reconstruct the brightness parameters in the color model space of the deblurred ship image. The experimental results show that the proposed method can effectively de blur the ship image, and the structural similarity between the processed image and the clear ship image is more than 0.89, and the information entropy is more than 9.1.
2022,44(22): 154-157 收稿日期：2022-06-07
DOI：10.3404/j.issn.1672-7649.2022.22.030
分类号：TP391
基金项目：福建能源器件科学与技术创新实验室开放基金资助项目（21C-OP-202013）
作者简介：周延木(1978-),男,硕士,讲师,从事计算机三维设计及视觉传达技术研究
参考文献：
[1] 陈卫东, 郭蔚然, 刘宏炜, 等. 基于改进Mask R-CNN的模糊图像实例分割的研究[J]. 电子与信息学报, 2020, 42(11): 2805–2812
CHEN Weidong, GUO Weiran, LIU Hongwei, et al. Research on fuzzy image instance segmentation based on improved mask R-CNN[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2805–2812
[2] 叶晓杰, 崔光茫, 赵巨峰, 等. 基于闪动快门的互补序列对的运动模糊图像复原[J]. 光子学报, 2020, 49(8): 161–175
YE Xiao-jie, CUI Guang-mang, ZHAO Ju-feng, et al. Motion blurred image restoration based on complementary sequence pair using fluttering shutter imaging[J]. Acta Photonica Sinica, 2020, 49(8): 161–175
[3] 梁晓萍, 郭振军, 朱昌洪. 基于头脑风暴优化算法的BP神经网络模糊图像复原[J]. 电子与信息学报, 2019, 41(12): 2980–2986
LIANG Xiaoping, GUO Zhenjun, ZHU Changhong. BP neural network fuzzy image restoration based on brain storming optimization algorithm[J]. Journal of Electronics & Information Technology, 2019, 41(12): 2980–2986
[4] 杨琼, 况姗芸, 冯义东. 基于全变差模型与卷积神经网络的模糊图像恢复[J]. 南京理工大学学报, 2022, 46(3): 277–283
YANG Qiong, KUANG Shanyun, FENG Yidong. Fuzzy image restoration based on TV model and CNN[J]. ournal of Nanjing University of Science and Technology, 2022, 46(3): 277–283
[5] 杨洁, 周洋, 谢菲, 等. 采用自适应梯度稀疏模型的图像去模糊算法[J]. 中国图象图形学报, 2019, 24(2): 180–191
YANG Jie, ZHOU Yang, XIE Fei, et al. Image deblurring using an adaptive sparse gradient model[J]. Journal of Image and Graphics, 2019, 24(2): 180–191
[6] 易开宇, 戴贞明. 基于混合非凸性二阶全变分和重叠组稀疏的非盲图像去模糊算法[J]. 电子测量与仪器学报, 2021, 35(9): 229–235
YI Kaiyu; DAI Zhenming. Non-blind image deblurring based on hybrid non-convex second-order total variation and the overlapping group sparse[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(9): 229–235
[7] 黄彦宁, 李伟红, 崔金凯, 等. 强边缘提取网络用于非均匀运动模糊图像盲复原[J]. 自动化学报, 2021, 47(11): 2637–2653
HUANG Yan-Ning, LI Wei-Hong, CUI Jin-Kai, et al. Strong edge extraction network for non-uniform blind motion image deblurring[J]. Acta Automatica Sinica, 2021, 47(11): 2637–2653
