Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, and many others. Such complex noise could degrade the quality of the acquired HSIs, limiting the precision of the subsequent processing. In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively. Specifically, for the clean HSI part, we use tensor Tucker decomposition to describe the global correlation among all bands, and an anisotropic spatial-spectral total variation (SSTV) regularization to characterize the piecewise smooth structure in both spatial and spectral domains. For the mixed noise part, we adopt the $\ell_1$ norm regularization to detect the sparse noise, including stripes, impulse noise, and dead pixels. Despite that TV regulariztion has the ability of removing Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian noise for some real-world scenarios. Then, we develop an efficient algorithm for solving the resulting optimization problem by using the augmented Lagrange multiplier (ALM) method. Finally, extensive experiments on simulated and real-world noise HSIs are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure

An Explorative Study of the Effectiveness of Mobile Advertising

This study examines factors related to the effectiveness of mobile advertising. Using a large data set with 115, 899 records of ad tap through from a mobile advertising company, we identify that the influencing factors for ad tap through are application type, mobile operators, scrolling frequency, and the regional income level. We use a logit model to analyze how the probability of ad tap through is related to the identified factors. The results show that application type, mobile operators, scrolling frequency, and the regional income level all have significant effects on the likelihood whether users would tap on certain types of advertising. Based on the findings, we propose strategies for mobile advertisers to engage in effective and targeted mobile advertising