Underwater images play a vital role in marine exploration, but are often severely degraded due to complex imaging conditions, including color distortion, haze effects, and non-uniform illumination. Existing deep learning-based enhancement methods predominantly rely on conventional RGB sensors, which struggle to distinguish between scattered and reflected light, thereby limiting enhancement performance. Polarization imaging, with its capability to capture directional light information, offers promising potential for underwater image enhancement. In this paper, we propose a lightweight yet effective polarization feature extractor that captures global spatial cues from polarization images. Additionally, we design a polarization-guided feature integration module that adaptively enhances the representational capacity of RGB features. Notably, the proposed module is plug-in and can be seamlessly integrated into existing RGB-based enhancement networks. Extensive experiments across multiple datasets demonstrate that incorporating polarization information significantly improves enhancement performance, highlighting its effectiveness as a valuable cue for underwater image enhancement. The code and pretrained models are at https://github.com/jgy0/UPGD.

Deep learning techniques are increasingly being employed for underwater video enhancement (UVE), with the key objectives not only encompassing color cast correction and image quality improvement but also maintaining temporal consistency. Most existing underwater image enhancement (UIE) methods, when directly applied to video sequences, often introduce inter-frame flickering artifacts, thereby impairing the comprehension of video content. Although 3D convolutional neural network-based approaches can preserve temporal coherence, they are characterized by substantial parameter counts and computational complexity, leading to inefficient model performance. To address these limitations, we propose a learnable Look-Up-Table (LUT) based on attenuation-aware and wavelet prior for UVE, abbreviated UVE-LUT, which effectively maintains inter-frame consistency in color and brightness distribution by leveraging look-up table (LUT) technology. The proposed method incorporates a learnable Attenuation-Aware LUT (AA-LUT) module that performs low-latency, low-complexity adaptive enhancement on the low-frequency components of frame sequences in the wavelet domain. Additionally, we design a high-frequency detail enhancement module to conduct three-dimensional detail enhancement on the high-frequency components. Extensive subjective and objective evaluations on underwater video and image datasets demonstrate that the proposed UVE-LUT achieves a favorable balance between image quality and temporal consistency. Our source code will be available at Github.

Underwater salient object detection (USOD) faces greater challenges than general scenes due to the edge blurring which is caused by light absorption and scattering in water. Existing methods employ unrefined edge feature to perform unidirectional guidance on saliency feature, resulting in the coarse edge of saliency map. To address this issue, we propose a novel interaction-driven edge crisping network (IDENet) for underwater salient object detection. IDENet facilitates the bidi-rectional modulation of inter-features and the self-refinement of intra-feature, generates crisp saliency map and edge map. In IDENet, the interaction-driven edge guidance module (IDEGM) is designed to utilize cross-feature interaction by leveraging their correlations, facilitating saliency feature’s awareness of edge information, mitigating the interference of non-salient objects in edge feature. To learn more accurate edge region of the salient object, the edge intersection-and-union loss function (EIUL) is introduced to restrict the intersection and union of predicted saliency maps and edge maps to prevent over-expansion or under-contraction. Experimental results on two latest underwater datasets demonstrate the superiority of the proposed method over the state-of-the-art models. The source code of our method will be made available at https://github.com/ UnderwaterVisionMZTdlmu/IDENet.

Event-driven eye-based emotion recognition has attracted increasing attention due to the high temporal resolution and dynamic range inherent to event cameras. The intrinsic spatial sparsity of event data, combined with the eye-based emotion recognition task's reliance on localized features such as eyebrows and eyelids, makes it intuitive and efficient to discard less informative regions. However, integrating such sparsification into CNNs remains challenging due to their reliance on dense grid-based operations. In this paper, we propose an efficient vision transformer framework for eye-based emotion recognition with event cameras. Specifically, we present window selection and token selection schemes tailored for event data and eye-based emotion recognition, which can diminish computing demands while enhancing performance. Firstly, we estimate the importance of all local windows and discard those with limited information, reducing computational cost while emphasizing attention on the periocular region. Secondly, we further introduce an adaptive token pruning mechanism that jointly evaluates the input event data and tokens to predict a binary decision mask, identifying and discarding uninformative tokens. Extensive experiments validate that the proposed approach outperforms existing state-of-the-art methods in accuracy by a significant margin.

A major challenge of long-term clock stability is frequency drift due to temperature variations. This paper describes the design of a proportional, integral, derivative (PID) control system for external ovenization of an AlScN-on-Si Shear-BAW Resonator (S3R), which has a fixed turnover temperature where the$1{^{\text {st}}}$order temperature coefficient of frequency is$\approx 0$ppm/°C. The control system provides$\pm ~0.125^{\circ }$C temperature stability and assists in achieving better than$\pm ~25$ppb frequency stability over a temperature range of 15-40°C by maintaining resonator operation near the turnover temperature, where the$2{^{\text {nd}}}$order temperature coefficient of frequency drift is -62.71ppb/°C2. The robust and adaptive PID algorithm (programmed on an external microcontroller unit connected to the interposer) ensures continuous ovenization by configuring the duty cycle of a compact heat actuator (powerMOS) that is placed in$\times 2$mm resonator and a complementary to absolute temperature sensor (implemented as a 1mm$\times 1$mm, 65nm integrated circuit), that are all held on a thermally conductive 7mm$\times$7mm Si interposer.

The attenuation and scattering of different colors of light underwater are wavelength- and distance-dependent, leading to various degradation problems in underwater images. When enhancing underwater images, many deep learning-based methods rely solely on convolutional neural networks to learn a mapping from degraded images to clear images to achieve enhanced effects. However, such methods have limitations in capturing long-term dependencies, preventing them from accurately capturing the global information of images. Although Transformers can solve this problem, there is a lack of inductive bias in training due to the limited number of training datasets with certain degradation phenomena. To address this issue, a novel Swin Transformer based on physical perception is proposed for the first time. Swin Transformer is used to solve the long- and short-distance dependency problem. Additionally, the underwater image degradation process is considered in network design to solve the problem of poor inductive bias. Combining the advantages of physical imaging, convolutional neural networks and Transformer can effectively improve the visual quality of underwater images. Rich qualitative and quantitative experimental results show that our Transformer achieves competitive performance on 5 benchmark datasets.

Due to light attenuation and complex environments, underwater robots need to carry artificial light to improve visibility, which leads to issues such as brightness vignetting, low contrast, and color distortion in the captured underwater images. However, existing methods for enhancing underwater images often overlook the challenges caused by artificial light. To address these challenges, we construct a novel underwater vignetting image formation model and propose a correction method called UVIC. The method consists of three main modules: separating the vignetting component, separating the backscattering component, and adaptive brightness and color correction. In our model, based on the linear relationship between the image gradient and the coefficients of the binary polynomial, we introduce a binary polynomial regularization to separate the vignetting component without estimating the center of the vignetting. Additionally, the backscattering can be effectively separated by introducing a latent low-rank representation based on local consistency, without estimating atmospheric light and transmission parameters. Furthermore, we design an adaptive brightness and color correction module using the global brightness of the image L layer and the histogram distribution characteristics of the a and b layers to adjust the brightness and color bias of the image. Particularly, there are both additive and multiplicative operations, and we decompose the objective function into two submodels and solve them by the iterative reweighted least squares and alternating direction multiplier methods, respectively. Numerous experiments demonstrate that UVIC not only effectively corrects image brightness vignetting, but also improves color bias, contrast, and sharpness.

Unsupervised hashing methods have attracted widespread attention with the explosive growth of large-scale data, which can greatly reduce storage and computation by learning compact binary codes. Existing unsupervised hashing methods attempt to exploit the valuable information from samples, which fails to take the local geometric structure of unlabeled samples into consideration. Moreover, hashing based on auto-encoders aims to minimize the reconstruction loss between the input data and binary codes, which ignores the potential consistency and complementarity of multiple sources data. To address the above issues, we propose a hashing algorithm based on auto-encoders for multiview binary clustering, which dynamically learns affinity graphs with low-rank constraints and adopts collaboratively learning between auto-encoders and affinity graphs to learn a unified binary code, called graph-collaborated auto-encoder (GCAE) hashing for multiview binary clustering. Specifically, we propose a multiview affinity graphs' learning model with low-rank constraint, which can mine the underlying geometric information from multiview data. Then, we design an encoder-decoder paradigm to collaborate the multiple affinity graphs, which can learn a unified binary code effectively. Notably, we impose the decorrelation and code balance constraints on binary codes to reduce the quantization errors. Finally, we use an alternating iterative optimization scheme to obtain the multiview clustering results. Extensive experimental results on five public datasets are provided to reveal the effectiveness of the algorithm and its superior performance over other state-of-the-art alternatives.

Nanomaterials possess superior mechanical, electrical, and optical properties suitable for device applications in different fields such as nanoelectronics, photonics, and sensors. Characterizing the multiphysical properties of single nanomaterials and nanostructures provides experimental guidelines for synthesis and device applications of functional nanomaterials. Nanomanipulation techniques under scanning electron microscopy (SEM) have enabled the testing of mechanical and electrical properties of various nanomaterials. However, the introduction of micro-photoluminescence ($\mu $-PL) measurement into an SEM setup for in-situ single nanomaterial characterization is still experimentally challenging; in particular, the seamless integration of the mechanical, electrical, and$\mu $-PL testing techniques inside an SEM for multi-field-coupled characterization of single nanostructures is still unexplored. In this work, we report the first SEM-based nanomanipulation system for multiphysical characterization of single nanomaterials. A custom-made, optical-microfiber-based$\mu $-PL setup is integrated onto a nanomanipulation system with four nanomanipulators inside an SEM. The system is also equipped with a conductive nanoprobe and a conductive atomic force microscopy (AFM) probe for electrical nanoprobing and electroluminescence (EL) measurement of single nanomaterials with contact force feedback. Using the system, field-coupled characterization (i.e., optomechanical, optoelectronic, electromechanical, and mechano-optoelectronic testing) of single InGaN/GaN nanowires (NWs) are conducted; and, for the first time, the effect of mechanical compression applied to individual InGaN/GaN NWs on its optoelectronic property is revealed. Note to Practitioners—With the rapid advances of nanophotonics and nanoelectronics, the optical and optoelectronic characterization of semiconductive nanomaterials becomes widely used for guiding the material synthesis and improving the nanodevice performance. However, few studies on optical-relevant characterization were carried out in SEM, mainly due to the limited space of an SEM chamber, making it challenging to integrate optical components for effective optical excitation and luminescence measurement. To address this issue, space-saving optical microfibers were integrated into the SEM chamber for in-situ optoelectronic characterization of semiconductor NWs, along with the seamless integration of mechanical and electrical nanoprobing tools for electromechanical characterization. The developed nanomanipulation system will greatly facilitate the multiphysical testing of semiconductor nanomaterials, and thus expedite their synthesis optimization processes and broaden their optoelectronic device applications.

Image taken in turbid media generally exists poor visibility and low contrast, which results from attenuation of the propagated light. In this letter, an effective polarization-based image dehazing method is proposed, which relies on the relationship between the angle of polarization (AoP) from the Stokes vector and the scattered light. To avoid the influence of noise, AoP is optimized based on regularization constraints. The regularization function is made using an assumption that adjacent pixels with similar colors have similar values of AoP. Moreover, according to the revised AoP information, all the key parameters can be effectively and automatically estimated without considering the no-object region (or the sky region) exists or not, which relies on a frequency prior strategy. Extensive experiments on real-world images demonstrate that the proposed method is more effective than several previous image restoration or enhancement works.

Images captured under low-light conditions are generally characterized by poor illumination, low contrast, and nonignorable large amount of noise. In order to improve the visibility in weak illumination scenes, multiple artificial light sources are used, which leads to severe uneven illumination of the scene. The main challenges of dehazing images with complex illumination are to suppress the boosting of unsightly noise when enhancing contrast and avoid overenhancement in bright glow regions. To circumvent problems above, this letter proposes a generalized enhancement framework, which works well not only in uniform light conditions but also in strongly nonuniform illumination low-light scenes. To achieve this, we first decompose the input hazy image into a structure layer containing low-frequency illumination variance and a texture layer containing large amount of high-frequency details. Sequentially, benefit from two derived masks that are intrinsically similar to weight maps, the proposed framework can perform regional adaptive brightness adjustment on the structure layer according to the distribution of light in the input image. Meanwhile, regions of effective details in the texture layer are assigned higher weights, while regions that belong to noise are suppressed. Finally, adding the enhanced texture layer back to the brightened structure layer, visually appealing results are generated. Experimental results on various scenarios demonstrate the superiority of the proposed framework over state-of-the-art methods in terms of both qualitative and quantitative.

Multi-view clustering aims to partition multi-view data into different categories by optimally exploring the consistency and complementary information from multiple sources. However, most existing multi-view clustering algorithms heavily rely on the similarity graphs from respective views and fail to comprehend multiple views holistically. Moreover, due to the noise and redundancy maintained in the original data, the original errors of multiple similarity graphs will continue to accumulate in the process of constructing consistent graphs. These situations always lead to the limitation to effective fuse the essential information from multiple views, which always influences the clustering performance and cries out for reliable solutions. Based on the above considerations, we propose a novel method termed Tensorial Multi-view Clustering (TMvC), which learns high-order graph by low-rank tensor constraint to uncover the essential information stored in multiple views. TMvC first learns the Laplacian graphs of all views and stacks them into a tensor which can be viewed as a high-order graph. With the high-order graph, consistency and complementary information from different views can be propagated smoothly across all views. Then, based on low-rank constraint, high-order graph is constrained in the horizontal and vertical directions to better uncover the inter-view and inter-class correlations between multi-view data, which is of vital importance for multi-view clustering. Extensive experiments on document and image datasets demonstrate that TMvC can achieve the state-of-the-art performance for multi-view clustering.

Functional nanomaterials possess exceptional multi-physical (e.g., mechanical, electrical and optical) properties compared with their bulk counterparts. To facilitate both synthesis and device applications of these nanomaterials, it is highly desired to characterize their multi-physical properties with high accuracy and efficiency. The nanomanipulation techniques under scanning electron microscopy (SEM) has enabled the testing of mechanical and electrical properties of various nanomaterials. However, the seamless integration of mechanical, electrical, and optical testing techniques into an SEM for triple-field-coupled characterization of single nanostructures is still unexplored. In this work, we report the first SEM-based nanomanipulation system for high-resolution mechano-optoelectronic testing of single semiconductor InGaN/GaN nanowires (NWs). A custom-made optical measurement setup was integrated onto a four-probe nanomanipulator inside an SEM, with two optical microfibers actuated by the nanomanipulator for NW excitation and emission measurement. A conductive tungsten nanoprobe and a conductive atomic force microscopy (AFM) cantilever probe were integrated onto the nanomanipulator for electrical nanoprobing of single NWs for electroluminescence (EL) measurement. The AFM probe also served as a force sensor for quantifying the contact force applied to the NW during nanoprobing. Using this unique system, we examined, for the first time, the effect of mechanical compression applied to an InGaN/GaN NW on its optoelectronic properties.

In natural rainy scenes, visibility is significantly degraded by two types of phenomena: specular highlights of nearby individual rain streaks and atmospheric veiling effect caused by distant accumulated rain. However, most existing deraining methods only take the first kind of degradation into consideration, which limits their potential application in heavy rain. In this study, a joint rain and atmospheric veil removal framework is proposed to address this problem. Since rain streaks and rain accumulation are entangled with each other, which is intractable to simulate, causing clean/rainy image pairs of real‐world are hard to generate. Hence, after introducing a generalised rain model, which can represent both rain streaks and atmospheric veil physically, the authors do not learn the mapping function between image pairs using deep‐learning architecture, but estimate the rain streaks, transmission, and atmospheric light via Gaussian mixture model patch prior and dark channel prior to solve the rain model instead. According to the comprehensive experimental evaluations, the proposed method outperforms other state‐of‐the‐art methods in terms of both high visibility and vivid colour, especially in natural heavy rain scenario.

Outdoor images captured under bad weathers often suffer from low visibility. In this study, a novel method is presented to improve the visibility of a single input hazy image. On the basis of the observation that degradation of a hazy image occurs both in contrast and colour, the authors method aims at compensating the contrast and colour of the image, respectively. To achieve this, they propose a multi‐scale gradient domain contrast enhancement approach that handles the different residual images rather than the entire image, and correct the attenuation of colour according to the estimated transmission. Since there is no need to recover the scene radiance by the degradation model, their method depends less on the accuracy of transmission and does not require the estimation of atmospheric light. Experiments on a variety types of hazy images show that their method yields accurate results with fine details and vivid colour, even better than other state‐of‐the‐art dehazing methods.

This paper provides a review of the recent developments of self-organized In(Ga)As/Ga(Al)As quantum dot lasers grown directly on Si, as well as their on-chip integration with Si waveguides and quantum-well electroabsorption modulators. A novel dislocation reduction technique, with the incorporation of self-organized In(Ga,Al)As quantum dots as highly effective three-dimensional dislocation filters, has been developed to overcome issues associated with the material incompatibility between III-V materials and Si. With the use of this technique, quantum dot lasers grown directly on Si exhibit relatively low threshold current (Jth=900 A/cm2) and very high temperature stability (T0=278 K). Integrated quantum dot lasers and quantum-well electroabsorption modulators on Si have been achieved, with a coupling coefficient of more than 20% and a modulation depth of ~100% at a reverse bias of 5 V. The monolithic integration of quantum dot lasers with both amorphous and crystalline Si waveguides, fabricated using plasma-enhanced chemical-vapor deposition and membrane transfer, respectively, has also been demonstrated.

Self-organized In(Ga)As/Ga(Al)As quantum dots have emerged as useful nanostructures that can be epitaxially grown and incorporated in the active region of devices. The near pyramidal dots exhibit properties arising from the three-dimensional quantum confinement and from the coherent built-in strain. The properties and current state-of-the-art characteristics of quantum-dot junction lasers, intersublevel infrared detectors, optical amplifiers, and microcavity devices are briefly reviewed. It is evident that self-organized quantum-dot optoelectronic devices demonstrate properties that are sometimes unique and often surpass the characteristics of existing devices.