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Compressive Sensing for Natural Images

7 pagesPublished: August 5, 2017

Abstract

The Compressive sensing technique is a new era of arising platform for signal processing and data acquisition. The significant statement of Compressive sensing is that recovery of certain images or signals from fewer samples than required. On the encoding side two properties of a signal is required that are incoherence and sparsity. Initially, the signal is converted into specific transform i.e. wavelet using sensing matrix it takes required coefficients that has less dimensionality than the image dimensions and thereby, we get resultant matrix which is also called as measurements which in turn are non-adaptive. Similarly, on the decoding side, due to low dimension of transmitted vector matrix, convex optimization is required to solve this problem. Convex optimization (L1 minimization) provides an answer to undetermined linear systems without the knowledge of nature of undergoing parameters through the systems.
In this paper, the work is being done on natural image compression. Compression of various black and white natural images is being done with help of ‘haar wavelet’ with second level decomposition and also reconstruct the same with three measurements of 60%, 70% and 80%. Also we have measured same with PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), CoC (Correlation Coefficients) of them.

Keyphrases: CoC (Correlation Coefficients), compressive sensing, Haar wavelet, l1 minimization, PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), sparsity

In: Ajitkumar Shukla, J. M. Patel, P. D. Solanki, K. B. Judal, R. K. Shukla, R. A. Thakkar, N. P. Gajjar, N. J. Kothari, Sukanta Saha, S. K. Joshi, Sanjay R. Joshi, Pranav Darji, Sanjay Dambhare, Bhupendra R. Parekh, P. M. George, Amit M. Trivedi, T. D. Pawar, Mehul B. Shah, Vinay J. Patel, Mehfuza S. Holia, Rashesh P. Mehta, Jagdish M. Rathod, Bhargav C. Goradiya and Dharita K. Patel (editors). ICRISET2017. International Conference on Research and Innovations in Science, Engineering and Technology. Selected Papers in Engineering, vol 1, pages 387--393

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BibTeX entry
@inproceedings{ICRISET2017:Compressive_Sensing_for_Natural,
  author    = {Mayur Sevak and Anish Bagga and Arpita Agarwal and Krusha Jani},
  title     = {Compressive Sensing for Natural Images},
  booktitle = {ICRISET2017. International Conference on Research and Innovations in  Science, Engineering and Technology. Selected Papers in Engineering},
  editor    = {Ajitkumar Shukla and J. M. Patel and P. D. Solanki and K. B. Judal and R. K. Shukla and R. A. Thakkar and N. P. Gajjar and N. J. Kothari and Sukanta Saha and S. K. Joshi and Sanjay R. Joshi and Pranav Darji and Sanjay Dambhare and Bhupendra R. Parekh and P. M. George and Amit M. Trivedi and T. D. Pawar and Mehul B. Shah and Vinay J. Patel and Mehfuza S. Holia and Rashesh P. Mehta and Jagdish M. Rathod and Bhargav C. Goradiya and Dharita K. Patel},
  series    = {Kalpa Publications in Engineering},
  volume    = {1},
  pages     = {387--393},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2515-1770},
  url       = {https://easychair.org/publications/paper/Nl},
  doi       = {10.29007/xjzx}}
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