We propose a method for local spectral component decomposition based on the line feature of local distribution. Our aim is to reduce noise on multi-channel images by exploiting the linear correlation in the spectral domain of a local region. We first calculate a linear feature over the spectral components of an M -channel image, which we call the spectral line, and then, using the line, we decompose the image into three components: a single M -channel image and two gray-scale images. By virtue of the decomposition, the noise is concentrated on the two images, and thus our algorithm needs to denoise only the two gray-scale images, regardless of the number of the channels. As a result, image deterioration due to the imbalance of the spectral component correlation can be avoided. The experiment shows that our method improves image quality with less deterioration while preserving vivid contrast. Our method is especially effective for hyperspectral images. The experimental results demonstrate that our proposed method can compete with the other state-of-the-art denoising methods.

Low-frequency dielectric properties of nematic liquid crystal (NLC) cells with and without polyimide alignment layers have been investigated with impedance spectroscopy. It is found that the dielectric behavior in the low frequency regime of NLC cells without polyimide alignment layers can be successfully explained in terms of the diffusive motion of impurity ions. The temperature dependences of the diffusion constant and the concentration of the impurity ions are determined from the dielectric behavior. NLC cells with polyimide alignment layers exhibit the interfacial polarization between the polyimide layers and NLC layer in the low frequency regime. The concentration of the impurity ions dissolved from polyimide layers to NLC layer is estimated.

Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the $L_{2,1}$ norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms.

A method has been proposed for the determination of localized‐state distributions in amorphous semiconductors from transient photoconductivity using Laplace transforms [H. Naito, J. Ding, and M. Okuda, Appl. Phys. Lett. 64, 1830 (1994)]. The method is applicable to both pre‐ and postrecombination regimes of transient photoconductivity, and to amorphous semiconductors exhibiting either nondispersive or dispersive transport. To simplify the analysis of the method, a new approach with the same advantages of the method is presented.

A simple method is presented for the determination of the rotational viscosity of nematic liquid crystals. Theoretical expressions for the rotational viscosity from transient displacement current in nematic liquid crystals are derived. The validity of the expressions is numerically examined. It is found that the rotational viscosity can be obtained with sufficient accuracy. The rotational viscosity of ZLI-2293 (Merck Ltd.) is measured at various temperatures by the present method.

This paper proposes a new optimization framework for the joint optimization of sparsity and filter order (JOSFO) for FIR filter design. Since the cost function for JOSFO involves ℓ ₀ and non-uniform overlapped group ℓ ₀ norms, which are not convex, a global optimal solution is difficult to obtain. To find an approximate solution of the non-convex problem, existing approaches repeat the following steps: 1) approximate the cost function; 2) find candidates of zero coefficients by minimizing the cost function; and 3) set them to zero. On the other hand, this paper directly solves the optimization problem, without any approximation to the cost function, by using the alternating direction method of multipliers with the pseudo-proximity operators of ℓ ₀ and non-uniform non-overlapped group ℓ ₀ norms. Experimental results show that resulting filters designed by the proposed method have sparser coefficients and lower orders, while satisfying filter specifications, such as an error from a desired frequency response.