Image compressive sensing reconstruction based on collaboration reduced rank preprocessing

Image compressive sensing reconstruction based on collaboration reduced rank preprocessing

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The problem of image compressive sensing (CS) preprocessing is considered. Currently, image CS reconstruction algorithms mainly consider the sparsity prior knowledge of original image. However, the change of the sparsity strength among the different images may degrade the efficiency of the reconstruction algorithms. Thus the idea of CS preprocessing is proposed to serve two purposes: strengthen the sparsity property of the CS measured image and make preprocessing and reconstruction algorithm matched. Specifically, the collaboration reduced rank (CRR) preprocessing is proposed based on non-local sparsity and non-local low-rank regularisation reconstruction algorithm (NLR-CS). Then a more efficient CRR-NLR-CS CS reconstruction method is proposed which utilises the CRR preprocessing and NLR-CS. Experimental results show the effectiveness of the proposed method.


    1. 1)
      • A. Chambolle .
        1. Chambolle, A.: ‘An algorithm for total variation minimization and applications’, J. Math. Imaging Vis., 2004, 20, (1–2), pp. 8997.
        . J. Math. Imaging Vis. , 89 - 97
    2. 2)
    3. 3)
    4. 4)
      • K. Egiazarian , A. Foi , V. Katkovnik .
        4. Egiazarian, K., Foi, A., Katkovnik, V.: ‘Compressed sensing image reconstruction via recursive spatially adaptive filtering’. IEEE Conf. on Image Processing, San Antonio, TX, 2007, pp. 522549.
        . IEEE Conf. on Image Processing , 522 - 549
    5. 5)
    6. 6)
      • M. Fazel , H. Hindi , S.P. Boyd .
        6. Fazel, M., Hindi, H., Boyd, S.P.: ‘Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices’. Proc. American Conf., June 2003, pp. 21562162.
        . Proc. American Conf. , 2156 - 2162
    7. 7)
    8. 8)

Related content

This article has following corresponding article(s):
in brief
This is a required field
Please enter a valid email address