Abstract
Compressive sensing (CS) is a technique that is very popular nowadays for compression and reconstruction. This technique is too efficient than the traditional methods for data compression. As per the Nyquist sampling theorem, for proper reconstruction of a signal, we have to do sampling at double the rate of bandwidth. Therefore, the storage which is required to store the signal is also very large. As a resultant, the cost effectiveness of the system reduces. The compressive sensing technique has the key feature to reduce this sampling rate by using the two parameters: basis and sensing matrices. In order to achieve this, there are two other important properties that are also discussed along with compressive sensing. The name of these properties are restricted isometry property (RIP) and independent and identically distributed (IID) property. For proper reconstruction of a signal, both these properties must be satisfied by the compressive sensing technique. In this paper a novel approach is applied on an image signal to measure the PSNR value with variation in basis and sensing matrices.
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References
Donoho D (2006) Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306
Baraniuk R (2007) Compressive sensing. IEEE Signal Process Mag 24(4):118–120, 124
Duarte Marco F, Eldar Yonina C (2011) Structured compressed sensing: from theory to applications. IEEE Trans Signal Process 59(9):4053–4085
Obermeier R (2016) Compressed sensing algorithms for electromagnetic imaging applications. PhD dissertation, Northeastern University
Hashempour HR, Masnadi-Shirazi MA, Arand BA (2017) Compressive sensing ISAR imaging with LFM signal. In: 2017 Iranian conference on electrical engineering (ICEE). IEEE, pp 1869–1873
Jerez A, Márquez M, Arguello H (2016) Compressive computed tomography image reconstruction by using the analysis of the internal structure of an object. In: 2016 XXI symposium on signal processing, images and artificial vision (STSIVA). IEEE, pp 1–5
Lakshminarayana M, Sarvagya M (2015) Random sample measurement and reconstruction of medical image signal using compressive sensing. In: 2015 international conference on computing and network communications (CoCoNet). IEEE, pp 255–262
Li X, Bi G, Stankovic S, Orovic I (2016) Improved Bayesian compressive sensing for image reconstruction using single-level wavelet transform. In: 2016 international conference on virtual reality and visualization (ICVRV). IEEE, pp 133–137
Deng C, Lin W, Lee B-S, Lau CT (2010) Robust image compression based on compressive sensing. In: 2010 IEEE international conference on multimedia and expo (ICME). IEEE, pp 462–467
Alonso, MT, López-Dekker P, Mallorquí JJ (2010) A novel strategy for radar imaging based on compressive sensing. IEEE Trans Geosci Remote Sens 48(12): 4285–4295
Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Info Theory 53(12):4655–4666
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Upadhyaya, V., Salim, M. (2020). Compressive Sensing: An Efficient Approach for Image Compression and Recovery. In: Sharma, H., Pundir, A., Yadav, N., Sharma, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0426-6_3
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DOI: https://doi.org/10.1007/978-981-15-0426-6_3
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