Localized surface plasmons (LSP), the confined collective excitations of electrons in noble metal and doped semiconductor nanostructures, enhance greatly local electric field near the surface of the nanostructures and result in strong optical response. LSPs of ordinary massive electrons have been investigated for a long time and were used as basic ingredient of plasmonics and metamaterials. LSPs of massless Dirac electrons, which could result in novel tunable plasmonic metamaterials in the terahertz and infrared frequency regime, are relatively unexplored. Here we report for first time the observation of LSPs in Bi2Se3 topological insulator hierarchical nanoflowers, which are consisted of a large number of Bi2Se3 nanocrystals. The existence of LSPs can be demonstrated by surface enhanced Raman scattering and absorbance spectra ranging from ultraviolet to near-infrared. LSPs produce an enhanced photothermal effect stimulated by near-infrared laser. The excellent photothermal conversion effect can be ascribed to the existence of topological surface states, and provides us a new way for practical application of topological insulators in nanoscale heat source and cancer therapy.

We report the design, characterization, and experimental demonstration of a broadband nearperfect absorber in the visible regime based on strong interference in ultrathin molybdenum disulfide films obtained by the spin-coating method. The absorber is polarization-insensitive, and the absorption peak maintains a high value for large angles of incidence, which provides more efficient absorption for nonpolarized or oblique incident beams. The experimental results show that the absorption can reach more than 87% between 400 nm and 640 nm, which is in reasonable agreement with the simulated results. This work may offer a further step in the development of solar absorption-based nano-optoelectronic devices. Published by AIP Publishing.

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In this Letter, a new algorithm is proposed to detect salient regions by combining spatial and spectral information. First, the input image is considered in both RGB color space and Lab color space. Second, the biggest symmetric surround model and spectral residual are calculated in each channel simultaneously. Third, the feature maps in some color channels outperform the feature maps in the other channels. Entropy is defined to evaluate the performance of the feature maps, which can be used to choose the proper channels and combine different feature maps. Finally, a Gaussian low-pass filter is applied to improve the performance by accounting for the center bias. Compared with previous methods, our saliency detection is more effective and robust as demonstrated by the experiments.

This paper is concerned with selective color feature for detecting salient regions. In contrast to most existing methods related to detection in one color space, the proposed algorithm pre-segments the input image into superpixels in both RGBY color space and Lab color space. Next, to calculate color contrast we not only consider the local feature, but also compute the difference between the pixel and the whole image. In the meanwhile, based on the center-surrounding scheme, a new computational model of color distribution features is presented to detect salient regions. Finally, 2D entropy is employed as an evaluation criterion to select and integrate the proper color features. Experimental results show that the proposed method outperforms the state-of-the-art methods on salient region detection.

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