Ramesh Kumar, Nandini and Xiang, Wei and Soar, Jeffrey (2011) A novel image compressive sensing method based on complex measurements. In: DICTA 2011: International Conference on Digital Image Computing: Techniques and Application, 6-8 Dec 2011 , Noosa, Australia.
Compressive sensing (CS) has emerged as an efficient signal compression and recovery technique, that exploits the sparsity of a signal in a transform domain to perform sampling and stable recovery. The existing image compression methods have complex coding techniques involved and are also vulnerable to errors. In this paper, we propose a novel image compression and recovery scheme based on compressive sensing principles. This is an alternative paradigm to conventional image coding and is robust in nature. To obtain a sparse representation of the input, discrete wavelet transform is used and random complex Hadamard transform is used for obtaining CS measurements. At the decoder, sparse reconstruction is carried out using compressive sampling matching pursuit (CoSaMP) algorithm. We show that, the proposed CS method for image sampling and reconstruction is efficient in terms of complexity, quality and is comparable with some of the existing CS techniques. We also demonstrate that our method uses considerably less number of random measurements.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||Permanent restricted access to published version due to publisher's copyright policy.|
|Uncontrolled Keywords:||compressive sensing; image representation; CS reconstruction; CoSaMP; complex Hadamard transforms|
|Depositing User:||Mrs Nandini Ramesh Kumar|
|Date Deposited:||26 Feb 2012 06:42|
|Last Modified:||16 Oct 2013 01:58|
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