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Metasurfaces, composed of planar arrays of intricately designed meta-atom structures, possess remarkable capabilities in controlling electromagnetic waves in various ways. A critical aspect of metasurface design involves selecting suitable meta-atoms to achieve target functionalities such as phase retardation, amplitude modulation, and polarization conversion. Conventional design processes often involve extensive parameter sweeping, a laborious and computationally intensive task heavily reliant on designer expertise and judgement. Here, we present an efficient genetic algorithm assisted meta-atom optimization method for high-performance metasurface optics, which is compatible to both single- and multi-objective device design tasks. We first employ the method for a single-objective design task and implement a high-efficiency Pancharatnam-Berry phase based metalens with an average focusing efficiency exceeding 80% in the visible spectrum. We then employ the method for a dual-objective metasurface design task and construct an efficient spin-multiplexed structural beam generator. The device is capable of generating zeroth-order and first-order Bessel beams respectively under right-handed and left-handed circular polarized illumination, with associated generation efficiencies surpassing 88%. Finally, we implement a wavelength and spin co-multiplexed four-channel metahologram capable of projecting two spin-multiplexed holographic images under each operational wavelength, with efficiencies over 50%. Our work offers a streamlined and easy-to-implement approach to meta-atom design and optimization, empowering designers to create diverse high-performance and multifunctional metasurface optics.

Abstract Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.

Abstract Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.

The Hongqiling is a vein-type Sn-W polymetallic deposit in southern Hunan (South China). It is geologically located on the northern margin of the Nanling metallogenic belt. Based on the mineral assemblage and vein crosscutting relationship, three mineralization stages were identified: Sn-W mineralization (S1: cassiterite, wolframite, scheelite, arsenopyrite, molybdenite, pyrite, chalcopyrite, and quartz), Pb-Zn mineralization (S2: chalcopyrite, pyrrhotite, galena, sphalerite, pyrite, quartz, and fluorite), and late mineralization (S3: quartz, fluorite, calcite, galena, sphalerite, and pyrite). According to laser Raman probe analysis, H2O dominates the fluid inclusions in the S1 and S2 stage quartz, with CO2 and trace N2 following close behind. The ore fluid has low salinity, low density, and a wide temperature range, as per our microthermometric data: the S1 stage has homogenization temperatures (Th) of 236–377.6 °C (average 305.3 °C) and salinity of 3.5–10.7 wt.% NaCleqv; the S2 stage has Th of 206.5–332 °C (average 280.7 °C) and salinity of 1.6–5.1 wt.% NaCleqv; and the S3 stage has Th of 170.9–328.7 °C (average 246 °C) and salinity of 0.2–5.9 wt.% NaCleqv. Based on the results of the aforementioned investigation, the fluid inclusions in quartz, fluorite, and calcite are mainly H2O-NaCl vapor-liquid two-phase. Additionally, examinations of inclusions in S1 wolframite and coexisting quartz using infrared and microthermometry show that the mineralizing fluid likewise belongs to the NaCl-H2O system. The Th of inclusions in wolframite is ~40 °C higher than that of coexisting quartz. Moreover, the fluid experienced a decrease in temperature accompanied by nearly constant salinity, which indicates that wolframite precipitation is due to fluid mixing and simple cooling, and the precipitation is earlier than quartz. In situ S and H-O isotope data show that the samples have δ34S = −2.58‰ to 1.84‰, and the ore fluids have δD = −76.6 to −51.5‰ (S1 and S2), and δ18Ofluid = −6.6 to −0.9‰ (S1) and −12.9 to −10.2‰ (S2). All these indicate that the mineralizing fluid was derived from the granitic magma at Qianlishan, with substantial meteoric water incursion during the ore stage. Such fluid mixing and subsequent cooling are most likely the primary controls for ore deposition.

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.