Computer Science ›› 2015, Vol. 42 ›› Issue (3): 316-320.doi: 10.11896/j.issn.1002-137X.2015.03.065

Previous Articles    

Image Restoration Algorithm of Unknown Priori Pixel Based on Artificial Fish Swarm Decomposition

SUI Dan and GAO Guo-wei   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Because unknown pixels lack prior information,module matching and edge structure information is unknown,and the holographic reconstruction is difficult.The traditional method uses multidimensional search method of subspace feature information,which failes to achieve the fine image texture template matching of structure information,and the effect is not good.Introducing the artificial fish swarm algorithm,this paper proposed a new image holographic image restoration algorithm based on artificial fish swarm micro decomposition and brightness compensation.Sub space feature information multidimensional search method is used for unknown pixel confidence updates.In order to maintain the continuity of damaged region in image,the artificial fish swarm algorithm decomposition model is constructed,combined with the edge feature of image brightness compensation strategy,and the image restoration algorithm is obtained.The simulation result shows that it has a good visual effect in image restoration of a priori unknown pixel,resulting in less recovery time and computation costs,and the stability and convergence performance are improved.SNR error is smaller within 6%,so it has superior performance in application.

Key words: Artificial fish swarm,Image restoration,Subspace,Brightness compensation

[1] 陶涛,夏新宇,李琳,等.面向数据融合计算的动画角色处理平台[J].合肥工业大学学报:自然科学版,2014,31(1):59-62
[2] Zhou Ya-tong,Li Lin,Xia Ke-wen.Research on weighted priority of exemplar-based image inpainting[J].Journal of Electro-nics,2012,29(1):166-170
[3] Xu Zong-ben,Sun Jian.Image inpainting by patch propagation using patch sparsity[J].IEEE Transaction on Image Proces-sing,2010,9(5):1153-1165
[4] Kwok Taz-Ho,Sheung H,Wang C C L.Fast query for exemplar-based image completion[J].IEEE Transactions on Image Processing,2010,9(12):3106-3115
[5] 孟春芝,何凯,焦青兰.自适应样本块大小的图像修复方法[J].中国图象图形学报,2012,7(3):337-341
[6] 曹健,李海生,蔡强.图像目标的特征提取技术研究[J].计算机仿真,2013,30(1):409-413
[7] 孙震,王兆霞,白明,等.基于自组织神经网络 SOM 和 K-means 聚类算法的图像修复[J].科学技术与工程,2012,12(8):1790-1794
[8] 罗海驰,李岳阳,孙俊.一种基于自适应神经模糊推理系统的图像滤波方法[J].计算机科学,2013,40(7):302-306
[9] 张芳芳.基于邻域梯度图像拼接算法研究[J].科技通报,2012,0(28):61-63
[10] 李志丹,和红杰,尹忠科,等.基于块结构稀疏度的自适应图像修复算法[J].电子学报,2013,1(3):549-554
[11] 王栋,尚堃.基于改进蚁群算法的红外图像边缘检测方法[J].四川兵工学报,2014,35(7):87-90

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!