A Coupled Non-local Total Variation Algorithm for Image Colorization
-
摘要: 基于局部算子的全变差(TV)模型在对纹理图像着色时，会出现颜色扩散不均匀，着色范围区域较小等问题。为了解决上述问题，该文提出基于非局部算子的耦合全变差图像着色模型，结合交替方向乘子法(ADMM)，设计出相应的数值求解算法，并给出该算法的收敛性结果。该模型充分利用像素邻域亮度之间的相似性进行颜色扩散，能有效避免仅利用亮度边缘信息进行局部扩散导致颜色扩散不均匀的问题。数值实验结果表明，该模型在快速着色的同时，能有效解决颜色在纹理等细节处扩散不均匀的问题。Abstract: The traditional Total Variation (TV) model based on local operators for texture image colorization has some problems, such as inhomogeneous color diffusion, small coloring ranges and so on. In order to solve these problems, a coupled total variation model based on nonlocal operators is presented for image colorization, and the correspond numerical algorithm is designed to solve the model by incorporating the Alternating Direction Method of Multipliers (ADMM), and the convergence result of the algorithm is given. The proposed model makes full use of the similarity between the brightness of the pixel areas to perform color diffusion, which can effectively avoid the problem of inhomogeneous color diffusion due to local diffusion only using the brightness edge information. The experimental results are given to show that the model can effectively solve the problem of inhomogeneous color diffusion at textures and other details while fast colorizing.
-
表 2 图3图像着色后的PSNR值及MSE值的比较
图像 模型 PSNR值 MSE值 第1排 Levin模型[4] 23.69 0.0043 Kang模型[8] 22.74 0.0053 金模型[9] 25.46 0.0028 本文模型 25.82 0.0026 第2排 Levin模型[4] 24.63 0.0034 Kang模型[8] 22.10 0.0062 金模型[9] 27.52 0.0018 本文模型 26.91 0.0020 第3排 Levin模型[4] 24.87 0.0033 Kang模型[8] 21.71 0.0067 金模型[9] 25.21 0.0030 本文模型 26.04 0.0025 第4排 Levin模型[4] 24.85 0.0033 Kang模型[8] 21.04 0.0079 金模型[9] 25.66 0.0027 本文模型 25.87 0.0026 
下载: 导出CSV
-
PAUL S, BHATTACHARYA S, and GUPTA S. Spatiotemporal colorization of video using 3D steerable pyramids[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27(8): 1605–1619 doi: 10.1109/TCSVT.2016.2539539 METE S P and DHAIT A S. A colorization algorithm with artifacts suppression on real time video[J]. International Journal of Science and Research, 2016, 5(4): 1126–1131 doi: 10.21275/v5i4.nov162748 HORIUCHI T. Colorization algorithm using probabilistic relaxation[J]. Image and Vision Computing, 2004, 22(3): 197–202 doi: 10.1016/j.imavis.2003.08.004 LEVIN A, LISCHINSKI D, and WEISS Y. Colorization using optimization[C]. Proceedings of ACM SIGGRAPH 2004, New York, USA, 2004: 689–694. SAPIRO G. Inpainting the colors[C]. IEEE International Conference on Image Processing, Genova, Italy, 2005: 6987–701. YATZIV L and SAPIRO G. Fast image and video colorization using chrominance blending[J]. IEEE Transactions on Image Processing, 2006, 15(5): 1120–1129 doi: 10.1109/tip.2005.864231 TENG Shenghua, CHEN An’jun and ZOU Mouyan. Colorization using Laplace equation[J].Journal of Image and Graphics, 2006, 11(4): 103–106 doi: 10.1002/qua.560410608 KANG S H and MARCH R. Variation models for image colorization via chromaticity and brightness decomposition[J]. IEEE Transactions on Image Processing, 2007, 16(9): 2251–2261 doi: 10.1109/tip.2007.903257 金正猛, 周晨. 基于耦合全变差的快速图像着色算法[J]. 电子学报, 2016, 44(10): 2364–2369 doi: 10.3969/j.issn.0372-2112.2016.10.012JIN Zhengmeng and ZHOU Chen. A fast coupled total variation algorithm for image colorization[J]. Acta Electronica Sinica, 2016, 44(10): 2364–2369 doi: 10.3969/j.issn.0372-2112.2016.10.012 GILBOA G and OSHER S. Nonlocal operators with applications to image processing[J]. Multiscale Model&Simul, 2009, 7(3): 1005–1028 doi: 10.1137/070698592 ZHU Wei, CHAYES V, TIARD A, et al. Unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5): 2786–2798 doi: 10.1109/tgrs.2017.2654486 陆文琪, 端金鸣, 魏伟波, 等. 基于非局部CTV-L1模型的大破损彩色纹理图像修复[J]. 计算机应用研究, 2016, 33(8): 2524–2529 doi: 10.3969/j.issn.1001-3965.2016.08.063LU Wenqi, DUAN Jinming, WEI Weibo, et al. Large broken color texture image inpainting using non-local CTV-L1 model[J]. Application Research of Computers, 2016, 33(8): 2524–2529 doi: 10.3969/j.issn.1001-3965.2016.08.063 LI Zhi, MALGOUYRES F, and ZENG Tieyong. Regularized non-local total variation and application in image restoration[J]. Journal of Mathematical Imaging and Vision, 2017, 59(2): 296–317 doi: 10.1007/s10851-017-0732-6 BUADES A, COLL B, and MOREL J M. A review of image denoising algorithms, with a new one[J]. Multiscale Model&Simul, 2005, 4(2): 490–530 doi: 10.1137/040616024 KOJU R and JOSHI S R. Comparative analysis of color image watermarking technique in RGB, YUV, and YCbCr Color Channels[J]. Nepal Journal of Science and Technology, 2015, 15(2): 133–140 doi: 10.3126/njst.v15i2.12130 CHELALI F Z, CHERABIT N, and DJERADI A. Face recognition system using skin detection in RGB and YCbCr color space[C]. World Symposium on Web Application & Networking, Sousse, Tunisia, 2015: 1–7. BOYD S, PARINKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2010, 3(1): 1–122 doi: 10.1561/ 2200000016 GLOWINSKI R and MARROCCO A. Sur lapproximation parele-ments finis dordreun, et la resolution par penalisation-du-alite dune classe de problemes de Dirichlet nonlineaires[J]. Revue Francaise D’Automatique,Informatique,Re-cherch Operationnel, 1975, 9(R2): 41–76 doi: 10.1051/m2an/197509r200411 期刊类型引用(20)
1. 周晨，周乾伟，陈翰墨，管秋，胡海根，吴延壮. 面向RGBD图像显著性检测的循环逐尺度融合网络. 小型微型计算机系统. 2023(10): 2276-2283 . 
百度学术2. 叶海峰，赵玉琛. 视觉位置识别中代表地点的标识牌算法. 小型微型计算机系统. 2021(04): 823-828 . 百度学术3. 王慧玲，宋鑫怡，杨颖. 基于优化查询的改进显著性检测算法. 吉林大学学报(信息科学版). 2020(03): 319-324 . 百度学术4. 郭迎春，李卓. 基于边缘特征和自适应融合的视频显著性检测. 河北工业大学学报. 2019(01): 1-7 . 百度学术5. 鲁文超，段先华，徐丹，王万耀. 基于多尺度下凸包改进的贝叶斯模型显著性检测算法. 计算机科学. 2019(06): 295-300 . 百度学术6. 王宝艳，张铁，李凯，杜松林. DEL分割算法对SSLS算法的改进. 小型微型计算机系统. 2019(10): 2052-2057 . 百度学术7. 张巧荣，徐国愚，张俊峰. 利用视觉显著性的前景目标分割. 兰州大学学报(自然科学版). 2019(06): 833-840 . 百度学术8. 杨俊丰，林亚平，欧博，蒋军强，李强. 基于显著性加权随机优化的快速响应码美化方法. 电子与信息学报. 2018(02): 289-297 . 
本站查看9. 邓晨，谢林柏. 全局对比和背景先验驱动的显著目标检测. 计算机工程与应用. 2018(03): 212-216 . 百度学术10. 刘亚宁，吴清，魏雪. 基于流行排序的前景背景显著性检测算法. 科学技术与工程. 2018(18): 74-81 . 百度学术11. 闫钧华，肖勇旗，姜惠华，杨勇，张寅. 融合区域像素显著性和时域信息的地面动目标检测及其DSP实现. 电子设计工程. 2018(19): 178-183+193 . 百度学术12. 陈厚仁，蔡延光. 基于视频的干线交通流检测系统的研究与实现. 工业控制计算机. 2017(07): 85-87 . 百度学术13. 赵艳艳，沈西挺. 基于同步更新的背景检测显著性优化. 计算机工程. 2017(10): 264-267 . 百度学术14. 田畅，姜青竹，吴泽民，刘涛，胡磊. 基于区域协方差的视频显著度局部空时优化模型. 电子与信息学报. 2016(07): 1586-1593 . 本站查看15. 罗会兰，万成涛，孔繁胜. 基于KL散度及多尺度融合的显著性区域检测算法. 电子与信息学报. 2016(07): 1594-1601 . 本站查看16. 张晴，林家骏，戴蒙. 基于图的流行排序的显著目标检测改进算法. 计算机工程与应用. 2016(22): 26-32+38 . 百度学术17. 杜永强. 过度曝光图像缺失信息修复算法. 科技通报. 2016(08): 146-149 . 百度学术18. 郎波，樊一娜，黄静. 利用混合高斯进行物体成分拟合匹配的算法. 科学技术与工程. 2016(20): 73-80 . 百度学术19. 项导，侯赛辉，王子磊. 基于背景学习的显著物体检测. 中国图象图形学报. 2016(12): 1634-1643 . 百度学术20. 吕建勇，唐振民. 一种基于图的流形排序的显著性目标检测改进方法. 电子与信息学报. 2015(11): 2555-2563 . 本站查看其他类型引用(21)
-
百度学术
图(3) / 表(2)
计量
- 文章访问数: 1774
- HTML全文浏览量: 571
- PDF下载量: 57
- 被引次数: 41
下载:
