DOI QR코드

DOI QR Code

Performance Comparison of BCS-SPL Techniques Against a Variety of Restoring Block Sizes

복원 블록 크기 변화에 따른 BCS-SPL기법의 이미지 복원 성능 비교

  • 류중선 (한밭대학교 멀티미디어공학과) ;
  • 김진수 (한밭대학교 정보통신공학과)
  • Received : 2016.06.06
  • Accepted : 2016.06.21
  • Published : 2016.06.30

Abstract

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. Specially, a block compressed sensing with Smoothed Projected Landweber (BCS-SPL) framework is one of the most widely used schemes. Currently, a variety of BCS-SPL schemes have been actively studied. However, when restoring, block sizes have effects on the reconstructed visual qualities, and in this paper, both a basic scheme of BCS-SPL and several modified schemes of BCS-SPL with structured measurement matrix are analyzed for the effects of the block sizes on the performances of reconstructed image qualities. Through several experiments, it is shown that a basic scheme of BCS-SPL provides superior performance in block size 4.

압축 센싱은 샤논/나이퀴스트 표본화 정리를 만족하는 나이퀴스트 율보다 더 적은 수의 표본화 주파수로 신호를 획득하더라도 그 신호가 성긴 신호라는 조건 하에 샘플링을 가능하게 하는 신호 처리 기술이다. 특히, BCS-SPL 구조는 가장 널리 사용되고 있는 방법 중에 한 가지이고, 현재에는 다양한 BCS-SPL 방식들이 연구되고 있다. 그러나 복원할 때, 블록크기는 복원 영상의 품질에 큰 영향을 미치고, 본 논문에서는 기본 구조와 더불어 구조화된 형태에 대해 다양한 블록 크기에 따라 성능을 비교한다. 다양한 실험 결과를 통하여 기본적인 구조의 BCS-SPL 알고리즘이 블록 크기 4일 때 가장 우수한 성능을 보여줌을 확인한다.

Keywords

References

  1. D. L. Donoho, "Compressed Sensing," IEEE Transactions on Information Theory, Vol 52, No. 4, pp. 1289-1306, Apr. 2006. https://doi.org/10.1109/TIT.2006.871582
  2. L. Gan, "Block Compressed Sensing of Natural Images," Proceedings of the International Conference on Digital Signal Processing, Cardiff, UK, pp. 403-406, July. 2007.
  3. S. Mun and J. E. Fowler, "Block Compressed Sensing of Images Using Directional Transforms," Proceedings of IEEE International Conference on Image Processing, USA, pp. 3021-3024, 2009.
  4. J. Zhang, D. Zhao, F. Jiang "Spatially Directional Predictive Coding for Blockbased Compressive Sensing of Natural Images," Proceedings of IEEE International Conference on Image Processing, pp. 1021-1025, Melbourne, Australia, Sep. 2013
  5. S. Mun, J. E. Fowler "Dpcm for Quantized Block-Based Compressed Sensing of Images," Proceedings of the European Signal Processing Conference, pp. 1424-1428, Aug. 2012
  6. C. Chen, E. W. Tramel, and J. E. Fowler, "Compressed Sensing Recovery of Images and Video Using Multihypothesis Predictions," Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 1193-1198, 2011.
  7. K. Q. Dinh, H. J. Shim, B. Jeon, "Measurement Coding For Compressive Imaging Using A Structural Measurement Matrix," Proceeding of the 20th International Conference on Image Processing, Melbourne, Australia, pp. 15-18, Sep. 2013.
  8. B. Jeon, "Compressed Sensing and Image Processing Application," Proceedings of The Magazine of the The Institute of Electronics and Information Engineers, Vol 41, No. 6, pp. 27-38, June. 2014.
  9. J. S. Ryu, J. S. Kim "An Effective Fast Algorithm of BCS-SPL Decoding Mechanism for Smart Imaging Devices." Journal of Korea Multimedia Society, Vol 19, No. 2, pp. 200-208, February. 2016. https://doi.org/10.9717/kmms.2016.19.2.200
  10. S. Yoo, "A Software Framework for Verifying Sensor Network Operations and Sensing Algorithms," Journal of the Korea Industrial Information System Society, Vol 17, No. 1, pp.63-71, 2012. https://doi.org/10.9723/jksiis.2012.17.1.063
  11. J. Kim and B. Lee, "Wave Information Retrieval Algorithm based on Iterative Refinement," Journal of the Korea Industrial Information System Society, Vol 21, No. 1, pp.7-15, 2016.
  12. S. Kwon and D. Lee, "Recognition Method of Multiple Objects for Virtual Touch Using Depth Information," Journal of the Korea Industrial Information System Society, Vo1 21, No. 1, pp.27-34, 2016.