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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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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