A recognition method in holographic data storage system by using structural similarity

被引:0
|
作者
Chen, Yu-Ta [1 ]
Ou-Yang, Mang [2 ]
Lee, Cheng-Chung [1 ]
机构
[1] Natl Cent Univ, Dept Opt & Photon, Jhongli, Taiwan
[2] Natl Chiao Tung Univ, Dept Elect & Comp Engn, Hsinchu, Taiwan
来源
OPTICS AND PHOTONICS FOR INFORMATION PROCESSING VII | 2013年 / 8855卷
关键词
structural similarity; holographic data storage system; recognition method; OPTICAL NOISE-REDUCTION;
D O I
10.1117/12.2022962
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The holographic data storage system (HDSS) is a page-oriented storage system with advantages of great capacity and high speed. The page-oriented recording breaks the tradition of the optical storage of one-point recording. As the signal image is retrieved from the storage material in the HDSS, various noises influences the image and then the data retrieve will be difficultly from the image by using the thresholding method. For progressing on the thresholding method, a recognition method, based on the structural similarity, is proposed to replace the thresholding method in the HDSS. The recognition method is implemented that the image comparison between the receive image and reference image is performed by the structural similarity method to find the most similar reference image to the received image. In the experiment, by using recognition method, the bit error rate (BER) results in 26% decrease less than using the thresholding method in the HDSS. Owing to some strong effects, such as non-uniform intensity and strong speckle, still influencing on the received image, the recognition method is seemed to be slightly better than thresholding method. In the future, the strong effects would be reduced to improve the quality of the receive image and then the result of using the recognition method may be vastly better than the thresholding method.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Objective Image Fusion Quality Evaluation Using Structural Similarity
    郑有志
    覃征
    Tsinghua Science and Technology, 2009, 14 (06) : 703 - 709
  • [32] Knowledge Graph Entity Alignment Using Relation Structural Similarity
    Peng, Yanhui
    Zhang, Jing
    Zhou, Cangqi
    Meng, Shunmei
    JOURNAL OF DATABASE MANAGEMENT, 2022, 33 (01)
  • [33] Image Structure Subspace Learning Using Structural Similarity Index
    Ghojogh, Benyamin
    Karray, Fakhri
    Crowley, Mark
    IMAGE ANALYSIS AND RECOGNITION, ICIAR 2019, PT I, 2019, 11662 : 33 - 44
  • [34] Assessment method to fusion effect based on structural similarity comparison in fusion images
    Zhang Yong
    Jin Weiqi
    Xue Rui
    INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND PATTERN RECOGNITION IN INDUSTRIAL ENGINEERING, 2010, 7820
  • [35] Comparing structural fingerprints using a literature-based similarity benchmark
    O'Boyle, Noel M.
    Sayle, Roger A.
    JOURNAL OF CHEMINFORMATICS, 2016, 8
  • [36] Improving feature location using structural similarity and iterative graph mapping
    Peng, Xin
    Xing, Zhenchang
    Tan, Xi
    Yu, Yijun
    Zhao, Wenyun
    JOURNAL OF SYSTEMS AND SOFTWARE, 2013, 86 (03) : 664 - 676
  • [37] Improving structural similarity based virtual screening using background knowledge
    Girschick, Tobias
    Puchbauer, Lucia
    Kramer, Stefan
    JOURNAL OF CHEMINFORMATICS, 2013, 5
  • [38] Improving structural similarity based virtual screening using background knowledge
    Tobias Girschick
    Lucia Puchbauer
    Stefan Kramer
    Journal of Cheminformatics, 5
  • [39] SPECULAR REFLECTION REMOVAL USING LOCAL STRUCTURAL SIMILARITY AND CHROMATICITY CONSISTENCY
    Zhao, Yongqiang
    Peng, Qunnie
    Xue, Jize
    Kong, Seong G.
    2015 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2015, : 3397 - 3401
  • [40] Comparing structural fingerprints using a literature-based similarity benchmark
    Noel M. O’Boyle
    Roger A. Sayle
    Journal of Cheminformatics, 8