Hybrid algorithms for spectral noise removal in hyper spectral images

Abdulghani Taha, Mohammed and Babu Loganathan, Ganesh (2020) Hybrid algorithms for spectral noise removal in hyper spectral images. AIP Conference Proceedings, 2271 (1).

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Abstract

The image acquired from a sensor is always degraded by some form of noise. The noise can be estimated and removed by the process of denoising. Recently, the use of Hybrid Algorithms for denoising has gained popularity. The most commonly used transformation are Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). DCT has the property of more energy compaction and requires less resources for computational whereas DWT is a multi-resolution transformation. The proposed Hybrid Algorithms take advantage of both of the algorithms and this reduces the false contouring and blocking articrafts effectively. In this paper, the Hybrid Algorithms are evaluated for various images by differentiating with respect to Mean Square Error, Peak Signal to Noise Ratio, Coefficient of Variance, Structural Similarity Index and Mean Structural Similarity Index.

Item Type: Article
Uncontrolled Keywords: CV, Denoising, MSE, PSNR, SSIM, MSSIM
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Q Science > QC Physics
Depositing User: ePrints deposit
Date Deposited: 08 Mar 2021 07:28
Last Modified: 08 Mar 2021 07:28
URI: http://eprints.tiu.edu.iq/id/eprint/414

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