Publication

Abstract : The presence of noise in hyperspectral images degrades the quality of applications to be carried out using these images. But, since a hyperspectral data consists of numerous bands, the total time requirement for denoising all the bands will be much higher compared to normal RGB or multispectral images. In this paper, a denoising technique based on Least Square (LS) weighted regularization is proposed. It is fast, yet efficient in denoising images. The proposed denoising technique is compared with Legendre-Fenchel (LF) denoising, Wavelet-based denoising, and Total Variation (TV) denoising methods based on computational time requirement and Signal-to-Noise Ratio (SNR) calculations. The experimental results show that the proposed LS-based denoising method gives as good denoising output as LF and Wavelet, but with far lesser time consumption. Also, edge details are preserved unlike in the case of total variation technique. © Springer Nature Singapore Pte Ltd. 2018.