SSFA-Net: Sparse strip and dual-domain spatial-frequency attention for efficient image dehazing

Target‐oriented efficient illumination imaging and acquisition design are investigated by combining controlled illumination and local plane wave decomposition. Based on the plane source synthesis and the local plane wave decomposition, the target‐oriented illumination‐controlled areal source is synthesized and analyzed, and ranges of efficient illumination sources and directions can be selected. Under the guidance of the range information, the target‐oriented efficient illumination source imaging and acquisition design can be implemented. For the target layer of Marmousi model with complex overlaying structures, the above target‐oriented efficient illumination source imaging method is applied, and the results show the feasibility and great potential of the method.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

We propose and demonstrate a compact and highly efficient single-pixel imaging system with data compression based on the traditional serial time-encoded amplified microscopy (STEAM) structure. Our proposal solves the big data issue and low-efficiency bottleneck in the conventional STEAM systems. Also, a cascaded Mach-Zehnder interferometric (MZI) structure as the optical random pattern generator is proposed and experimentally demonstrated as the low-cost approach to achieve data compression. A 45° tilted fiber grating (TFG) is applied to increase the efficiency of the imaging system. A state-of-art experiment is performed and data compression ratios from 5% to 20% at an imaging size of 1mm*1.8mm are conducted using our proposed system for the first time to the best of our knowledge. The proposed optical imaging system mitigates the big data issue, decreases the system cost, and increases energy efficiency, and lays a solid foundation for low-cost, compact, and highly efficient ultrafast optical imaging.

Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions.

Rational design the energy bridge of organic ligands in scintillating lanthanide coordination polymers improved conversion efficiency to enhance characteristic emission of lanthanide cations, achieving high efficient X-ray imaging.

Elevated nitric oxide (NO) levels perform an important pathological role in various inflammatory diseases. Developing NO-activatable theranostic materials with a two-photon excitation (TPE) feature is highly promising for precision imaging and therapy, but constructing such materials is still a tremendous challenge. Here, we present the first example of a NO-activatable fluorescent photosensitizer (DBB-NO) accompanying extremely NO-elevated two-photon absorption (TPA) for efficient fluorescence imaging and photodynamic therapy (PDT). Upon responding to NO, DBB-NO shows not only a remarkably enhanced fluorescence quantum yield (ΦF, 0.17% vs. 9.3%) and singlet oxygen quantum yield (ΦΔ, 1.2% vs. 82%) but also an extremely elevated TPA cross-section (δ, 270 vs. 2800 GM). Simultaneous enhancement of ΦΔ, ΦF and δ allows unprecedented two-photon fluorescence brightness (δ × ΦF = 260.4 GM) and two-photon PDT (TP-PDT) efficiency (δ × ΦΔ = 2296 GM) which precedes the value for a commercial two-photon photosensitizer by two orders of magnitude. With these merits, the proof-of-concept applications of NO-activatable two-photon fluorescence imaging and TP-PDT in activated macrophages (in which NO is overproduced) were readily realized. This work may open up many opportunities for constructing two-photon theranostic materials with other pathological condition-activatable features for precise theranostics.

An efficient and effective blind camera image quality metric via modeling quaternion wavelet coefficients

Cell-based immunotherapy has emerged as a promising therapy for the treatment of cancer. Measuring the changes in tumor volume and tumor markers post treatment has been the most common means of evaluating the therapeutic efficacy. In order to assess the consequences of a given therapy in real time, the development of efficient molecular probes and imaging modalities are urgently needed. Efficient molecular probes and imaging modalities will provide qualitative and quantitative real-time images with long-term stability in physiological conditions as well as low toxicity and high sensitivity for in vivo monitoring of the transplanted cells. Therapeutic cells can be intrinsically or extrinsically modified with proper molecular probes, amplified in vitro, and transferred back into the host. In this chapter, we will discuss the relative strengths and weaknesses of multiple molecular imaging modalities as well as recent advances in molecular imaging probes. We will also address their application in relation to in vivo tracking of dendritic cells (DCs), natural killer (NK) cells, and T cells. Noninvasive molecular imaging techniques have great potential in the diagnostic and prognostic assessments of patients.KeywordsImmune cell trackingMolecular imaging modalitiesNanoprobes

We propose a deep-learning-based illumination analysis and efficient local imaging method. Based on the wavefield forward modeling, seismic illumination can intuitively express the energy propagation of direct waves, reflected waves, and transmitted waves, while it requires high calculation costs. We use a series of convolution operations in deep learning to establish the nonlinear relationship between the model and the illuminations to realize single-shot illumination result of the model. Stacking the single shot illumination results obtained by the network prediction can further help determine the target area. For the target area, we use a deep learning method to obtain the low illumination area of the geological model. Each shot has contribution to the low illuminated area; single shot is selected based on the contribution of the shot being greater than the average illuminance, and the low illumination area is imaged by reverse time migration on the selected shot gather. The trained convolutional neural network can help us quickly obtain the single shot illumination result of the model, which is convenient to analyze the energy distribution of various areas of geological model, and do further imaging for target areas. Using part of the shot gathers to image a local area can recover the complex geological structure of the area and improve the efficiency of reverse time migration especially for 3D problems. This method has universal applicability and is suitable for local imaging of various complex models such as subsalt areas and deep regions.

Augmented reality (AR) displays, heralded as the next-generation platform for spatial computing, metaverse, and digital twins, empower users to perceive digital images overlaid with real-world environment, fostering a deeper level of human-digital interactions. With the rapid evolution of couplers, waveguide-based AR displays have streamlined the entire system, boasting a slim form factor and high optical performance. However, challenges persist in the waveguide combiner, including low optical efficiency and poor image uniformity, significantly hindering the long-term usage and user experience. In this paper, we first analyze the root causes of the low optical efficiency and poor uniformity in waveguide-based AR displays. We then discover and elucidate an anomalous polarization conversion phenomenon inherent to polarization volume gratings (PVGs) when the incident light direction does not satisfy the Bragg condition. This new property is effectively leveraged to circumvent the tradeoff between in-coupling efficiency and eyebox uniformity. Through feasibility demonstration experiments, we measure the light leakage in multiple PVGs with varying thicknesses using a laser source and a liquid-crystal-on-silicon light engine. The experiment corroborates the polarization conversion phenomenon, and the results align with simulation well. To explore the potential of such a polarization conversion phenomenon further, we design and simulate a waveguide display with a 50° field of view. Through achieving first-order polarization conversion in a PVG, the in-coupling efficiency and uniformity are improved by 2 times and 2.3 times, respectively, compared to conventional couplers. This groundbreaking discovery holds immense potential for revolutionizing next-generation waveguide-based AR displays, promising a higher efficiency and superior image uniformity.

Sparse reconstruction methods have been successfully applied for efficient radar imaging of targets embedded in stratified dielectric subsurface media. Recently, a total variation minimization (TVM) based approach was shown to provide superior image reconstruction performance over standard L1-norm minimization-based method, especially in case of non-point-like targets. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for embedded target extent. In this paper, we provide qualitative and quantitative performance evaluations of TVM and GSR schemes for efficient and reliable target imaging in stratified subsurface media. Using numerical electromagnetic data of targets buried in the ground, we demonstrate that GSR and TVM provide comparable reconstruction performance qualitatively, with GSR exhibiting a slight superiority over TVM quantitatively, albeit at the expense of less flexibility in regularization parameters.

An efficient ultrasonic wavenumber-domain plane wave imaging method towards the inspection of curved structures

Due to the large range cell migration (RCM) and highly-nonstationary Doppler frequency modulation (DFM) of rapidly spinning targets, it is difficult to efficiently obtain well-focused inverse synthetic aperture (ISAR) images via conventional methods. Inspired by the azimuth spatial invariance in strip-map synthetic aperture radar (SAR) imaging mode, in this paper, an efficient range-Doppler domain ISAR imaging methods for rapidly spinning target is proposed. First, the echo signal is transformed into range-Doppler domain and its precise analytical expression in range-Doppler domain is derived according to the principle of stationary phase (POSP). Second, the energy of scatterers distributed in different range cells is extracted along the rotating radius to concentrate it into the same range cell. After that, the high-order phase terms of signal are compensated. Finally, the scatterer is focused in two-dimensional time domain. The simulated results demonstrate the effectiveness of the proposed method.

Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.

ISAR imaging for a marine moving target faces a series of challenges in the airborne radar system, especially the strong clutter interference with Doppler frequency spreading. To alleviate such problems, this paper proposes an efficient ISAR imaging method based on sliding window STAP. In the method, the whole CPI is divided into a series of coherent processing sub-intervals (CPSIs). Those CPSIs are generated with sliding window technique and they have identical length. In each CPSI, sub-CPI STAP is adopted to suppress the clutter. After that, the target signal is enhanced in terms of signal-to-clutter-plus-noise ratio (SCNR). Meanwhile, the Doppler frequency linear differences with respect to the azimuth dimension is still maintained, which contributes to the further ISAR imaging by Range-Doppler (RD) algorithm after range migration correction. Comparing with existing full-CPI STAP based method, the proposed method improves ISAR imaging performance while requiring less computational complexity. The simulation experiments are carried out to verify the effectiveness of the proposed method.