Hyperspectral Images (HSI) are usually affected by different type of noises such as Gaussian and non-Gaussian. The existing noise can directly affect the classification, unmixing and superresolution analyses. In this paper, the effect of denoising on superresolution of HSI is investigated. First a denoising method based on shearlet transform is applied to the low-resolution HSI in order to reduce the effect of noise, then the superresolution method based on Bayesian sparse representation is used. The proposed method is applied to real HSI dataset. The obtained results of the proposed method in comparison with some of the state-of-the-art superresolution methods show that the proposed method significantly increases the spatial resolution and decreases the noise effects efficiently.
Armin Eskandari and Azam Karami, "The effect of denoising on superresolution of hyperspectral imaging," Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 1042708 (Presented at SPIE Remote Sensing: September 11, 2017; Published: 4 October 2017); https://doi.org/10.1117/12.2278503.
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