Zhang, F; Yang, G; Xue, J-H; (2020) Hyperspectral image denoising based on low-rank coefficients and orthonormal dictionary. Signal Processing , 177 , Article 107738. 10.1016/j.sigpro.2020.107738.

Preview

Text FanlongZhang-SIGPRO-accepted.pdf - Accepted Version Download (21MB) | Preview

Abstract

Hyperspectral images (HSIs) are unavoidably contaminated by noise during data acquisition and transmission. A variety of noise reduction approaches have been developed for HSIs, in which the low-rank based methods have emerged as a powerful tool. However, most of low-rank based HSI denoising methods are designed to explore the low-rankness in the pixel space under a fixed dictionary, without seeking the optimal dictionary and exploiting the low-rankness of representation coefficients. In this work, a novel HSI denoising model based on Low-Rank Coefficients and Orthonormal Dictionary (LRCOD) is proposed. The novelty of this work lies in LRCOD’s exploiting the low-rankness of representation coefficients and learning an orthonormal dictionary for HSI denoising. To solve the proposed model, an efficient alternating minimization algorithm is developed, since one sub-problem has a closed-form solution and the other can be efficiently solved by the curvilinear search algorithm. Compared with gradient-type algorithms, our algorithm is easy to implement as there is no need to tune optimization parameters like step sizes. The experimental results over both simulated and real datasets verified the superiority of the proposed method. Specifically, under various levels of noise in different scenes, LRCOD yields the best MPSNR in 30 of 35 cases and runs faster than other methods except one non-iterative algorithm.

Download activity - last month

Export as XMLJSONCSV

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item