Search Results (26)

Image quality assessment (IQA) aims to measure the consistency between an objective algorithm output and a subjective perception measurement. This article focuses on this complex relationship in the context of color image scenarios—color image quality assessment (CIQA). This review systematically investigates CIQA applications in image compression, processing optimization, and domain-specific scenarios, analyzes benchmark datasets and assessment metrics, and categorizes CIQA algorithms into full-reference (FR), reduced-reference (RR) and no-reference (NR) methods. In this study, color images are evaluated using a newly developed CIQA framework. Focusing on FR and NR methods, FR methods leverage reference images with machine learning, visual perception models, and mathematical frameworks, while NR methods utilize distortion-only features through feature fusion and extraction techniques. Specialized CIQA algorithms are developed for robotics, low-light, and underwater imaging. Despite progress, challenges remain in cross-domain adaptability, generalization, and contextualized assessment. Future directions may include prototype-based cross-domain adaptation, fidelity–structure balancing, spatiotemporal consistency integration, and CIQA–restoration synergy to meet emerging demands. Full article

The integration of in vitro biological neural networks (BNNs) with robotic systems to explore their information processing and adaptive learning in practical tasks has gained significant attention in the fields of neuroscience and robotics. However, existing BNN-based robotic systems cannot perceive the visual environment due to the inefficiency of sensory information encoding methods. In this study, we propose a biomimetic visual information spatiotemporal encoding method based on improved delayed phase encoding. This method transforms high-dimensional images into a series of pulse sequences through convolution, temporal delay, alignment, and compression for BNN stimuli. We conduct three stages of unsupervised training on in vitro BNNs using high-density microelectrode arrays (HD-MEAs) to validate the potential of the proposed encoding method for image recognition tasks. The neural activity is decoded via a logistic regression model. The experimental results show that the firing patterns of BNNs with different spatiotemporal stimuli are highly separable in the feature space. After the third training stage, the image recognition accuracy reaches 80.33% ± 7.94%, which is 13.64% higher than that of the first training stage. Meanwhile, the BNNs exhibit significant increases in the connection number, connection strength, and inter-module participation coefficient after unsupervised training. These results demonstrate that the proposed method significantly enhances the functional connectivity and cross-module information exchange in BNNs. Full article

Higher spatial and temporal resolutions are two important performance parameters in an imaging system. However, due to hardware limitations, the two resolutions are usually mutually restricted. To meet this challenge, we propose a spatiotemporal compressive imaging (STCI) system to reconstruct high-spatiotemporal-resolution images from low-resolution measurements. For STCI, we also designed a novel reconstruction network for multiple compression ratio (CR). To verify the effectiveness of our method, we implemented simulation and optical experiments, respectively. The experiment results show that our method can effectively reconstruct high-spatiotemporal-resolution target scenes for nine different CRs. With the maximum spatiotemporal CR of 128:1, our method can achieve a reconstruction accuracy of 28.28 dB. Full article

An ultrasound-responsive hydrogel system was developed that provides on-demand release when stimulated by focused ultrasound (fUS). Diels–Alder cycloadducts crosslinked polyethylene glycol (PEG) hydrogels and underwent a retrograde Diels–Alder reaction when exposed to fUS. Four-arm and eight-arm furan-based Diels–Alder hydrogel compositions were used to evaluate the link between the crosslinking density and the fUS-induced release and retention rates. PEG crosslinked with glutaraldehyde was also used as a non-Diels–Alder control hydrogel. By increasing the exposure time and the amplitude of fUS, the Diels–Alder-based hydrogels exhibited a correlative increase in the release of the entrapped BMP-2. Real-time B-mode imaging was used during fUS to visualize the on-demand degradation of the crosslinking matrix for the release of BMP-2. When monitored with a thermocouple, the increase in temperature observed was minimal in the area surrounding the sample during fUS stimulation, indicating fUS to be an external stimulus which could be used safely for spatiotemporally controlled release. PEG hydrogels were characterized using nuclear magnetic resonance, Fourier transform infrared spectroscopy, differential scanning calorimetry, thermogravimetric analysis, and compression testing. PEG degradation byproducts were evaluated for cytocompatibility in vitro. Overall, this study demonstrated that Diels–Alder-based PEG hydrogels can encapsulate BMP-2, undergo a retrograde reaction when externally stimulated with fUS, and release active BMP-2 to induce differentiation in human mesenchymal stem cells. Full article

Event cameras, as bio-inspired visual sensors, offer significant advantages in their high dynamic range and high temporal resolution for visual tasks. These capabilities enable efficient and reliable motion estimation even in the most complex scenes. However, these advantages come with certain trade-offs. For instance, current event-based vision sensors have low spatial resolution, and the process of event representation can result in varying degrees of data redundancy and incompleteness. Additionally, due to the inherent characteristics of event stream data, they cannot be utilized directly; pre-processing steps such as slicing and frame compression are required. Currently, various pre-processing algorithms exist for slicing and compressing event streams. However, these methods fall short when dealing with multiple subjects moving at different and varying speeds within the event stream, potentially exacerbating the inherent deficiencies of the event information flow. To address this longstanding issue, we propose a novel and efficient Asynchronous Spike Dynamic Metric and Slicing algorithm (ASDMS). ASDMS adaptively segments the event stream into fragments of varying lengths based on the spatiotemporal structure and polarity attributes of the events. Moreover, we introduce a new Adaptive Spatiotemporal Subject Surface Compensation algorithm (ASSSC). ASSSC compensates for missing motion information in the event stream and removes redundant information, thereby achieving better performance and effectiveness in event stream segmentation compared to existing event representation algorithms. Additionally, after compressing the processed results into frame images, the imaging quality is significantly improved. Finally, we propose a new evaluation metric, the Actual Performance Efficiency Discrepancy (APED), which combines actual distortion rate and event information entropy to quantify and compare the effectiveness of our method against other existing event representation methods. The final experimental results demonstrate that our event representation method outperforms existing approaches and addresses the shortcomings of current methods in handling event streams with multiple entities moving at varying speeds simultaneously. Full article

Microwave coincidence imaging (MCI) represents a novel forward-looking radar imaging method with high-resolution capabilities. Most MCI methods rely on random frequency modulation to generate stochastic radiation fields, which introduces the complexity of radar systems and imposes limitations on imaging quality under noisy conditions. In this paper, microwave coincidence imaging with phase-coded stochastic radiation fields is proposed, which generates spatio-temporally uncorrelated stochastic radiation fields with phase coding. Firstly, the radiation field characteristics are analyzed, and the coding sequences are designed. Then, pulse compression is applied to achieve a one-dimensional range image. Furthermore, an azimuthal imaging model is built, and a reference matrix is derived from the frequency domain. Finally, sparse Bayesian learning (SBL) and alternating direction method of multipliers (ADMM)-based total variation are implemented to reconstruct targets. The methods have better imaging performance at low signal-to-noise ratios (SNRs), as shown by the imaging results and mean square error (MSE) curves. In addition, compared with the SBL and ADMM-based total variation methods based on the direct frequency-domain solution, the proposed method’s computational complexity is reduced, giving it great potential in forward-looking high-resolution scenarios, such as autonomous obstacle avoidance with vehicle-mounted radar and terminal guidance. Full article

Obtaining river or sea pebbles from local resources for concrete production is considered an economical and eco-friendly alternative, particularly in marine and island-offshore engineering. However, the resulting changes in the mechanical properties of these concrete have attracted attention. This study investigates the compressive behavior of concretes where river or sea pebbles partially (i.e., 33% and 67%) or fully (i.e., 100%) replace traditional gravel as coarse aggregate, using a noncontact full-field deformation measurement system based on digital image correlation (DIC). Compared to the traditional gravel concrete (GC), compressive strengths of the river pebble concrete (RPC) at constitution rates of 33%, 67%, and 100% decreased by 6.5%, 29.8%, and 38.9% while those values of the sea pebble concrete (SPC) decreased by 13.1%, 32.7%, and 44.3%, respectively. Meanwhile, SPC exhibited slightly lower compressive strength than RPC. The peak strains of both SPC and RPC decreased at lower substitution rates, although their stress-strain curves resembled those of GC. In contrast, RPC and SPC at higher substitution rates exhibited a noticeable stage of load hardening. Full-field deformation data and interfacial characteristics indicated that the compressive failure modes of both RPC and SPC showed significant interfacial slipping between pebbles and mortar with increasing coarse aggregate substitution rates. In comparison, fractures in coarse aggregate and mortar were observed in damaged GC. The study demonstrated that the spatio-temporal compressive deformation response and failure modes of SPC and RPC were distinct due to the introduction of pebbles, providing insights for engineering applications of river/sea pebble concrete in practical offshore or island construction projects. Full article

Compression corner shock wave/boundary layer interaction (SWBLI) is a typical shock wave/boundary layer interaction (SWBLI) problem in supersonic/hypersonic flows. In previous studies, the separation flow is usually caused by a single shock wave. However, in the actual aircraft surface configuration, two-stage compression or even multistage compression will produce more complex SWBLI problems. The multi-channel shock structure makes the flow field structure more complicated and also puts forward higher requirements for the flow control scheme. In order to explore a flow control method for the double compression corner shock wave/boundary layer interaction problem, an experimental study is carried out to control the double compression corner shock wave/boundary layer interaction with a high-energy flow pulsed arc discharge array under the condition that the incoming flow velocity Ma 6.0 has both noise flow fields and quiet flow fields. The results show that when U_DC = 0.5 kV actuation is applied, the influence range of the hot gas mass flow direction is about 65 mm, which can weaken the shock wave intensity to a certain extent. When U_DC = 1 kV actuation is applied, the influence range of the hot gas mass flow direction extends to 85 mm, and the actuation has a significant control effect on the flow field. Through spatio-temporal evolution analysis and spatial gradient threshold processing of high-speed schlieren images of actuated flow fields, the feasibility of controlling the hypersonic double compression corner shock wave/boundary layer interaction by using a high-energy flow pulsed arc discharge array is verified. The control law of a high-energy flow pulsed arc discharge array acting on the double compression corner shock wave/boundary layer interaction is revealed. Full article

A camera captures multidimensional information of the real world by convolving it into two dimensions using a sensing matrix. The original multidimensional information is then reconstructed from captured images. Traditionally, multidimensional information has been captured by uniform sampling, but by optimizing the sensing matrix, we can capture images more efficiently and reconstruct multidimensional information with high quality. Although compressive video sensing requires random sampling as a theoretical optimum, when designing the sensing matrix in practice, there are many hardware limitations (such as exposure and color filter patterns). Existing studies have found random sampling is not always the best solution for compressive sensing because the optimal sampling pattern is related to the scene context, and it is hard to manually design a sampling pattern and reconstruction algorithm. In this paper, we propose an end-to-end learning approach that jointly optimizes the sampling pattern as well as the reconstruction decoder. We applied this deep sensing approach to the video compressive sensing problem. We modeled the spatio–temporal sampling and color filter pattern using a convolutional neural network constrained by hardware limitations during network training. We demonstrated that the proposed method performs better than the manually designed method in gray-scale video and color video acquisitions. Full article

For compressed images and videos, quality enhancement is essential. Though there have been remarkable achievements related to deep learning, deep learning models are too large to apply to real-time tasks. Therefore, a fast multi-frame quality enhancement method for compressed video, named Fast-MFQE, is proposed to meet the requirement of video-quality enhancement for real-time applications. There are three main modules in this method. One is the image pre-processing building module (IPPB), which is used to reduce redundant information of input images. The second one is the spatio-temporal fusion attention (STFA) module. It is introduced to effectively merge temporal and spatial information of input video frames. The third one is the feature reconstruction network (FRN), which is developed to effectively reconstruct and enhance the spatio-temporal information. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of lightweight parameters, inference speed, and quality enhancement performance. Even at a resolution of 1080p, the Fast-MFQE achieves a remarkable inference speed of over 25 frames per second, while providing a PSNR increase of 19.6% on average when QP = 37. Full article

The instability and failure of rock that has been frozen and thawed cause serious rock engineering accidents in cold regions. Exploring the precursor information of freeze–thaw rock failure is of great theoretical value and engineering significance. Real-time uniaxial compression acoustic thermal monitoring experiments were conducted on freeze–thaw sandstone, and non-contact rock fracture precursor warning indicators were proposed. According to the coupled analysis of acoustic–thermal monitoring information, a precursor information chain for freeze–thaw rocks was established in time and space, and the spatiotemporal evolution of damage and acoustic thermal effects of freeze–thaw sandstone under compressive load was studied. The freeze–thaw cycle enhances the sensitivity of acoustic–thermal precursor information. Significant synchronous changes in ring count often occur during the rapid expansion period of damage, which can provide an essential reference for the occurrence and intensification of damage. The sequence of precursor warning information during the process of freeze–thaw sandstone compression failure is in the order of thermal infrared temperature → acoustic emission ringing count → acoustic emission energy → infrared thermal image. Thermal infrared temperature and acoustic emission precursor information can help in prioritizing early warning of rock damage in terms of time. At the same time, thermal image anomalies can predict potential fracture areas of rocks. Full article

Video Snapshot Compressive Imaging (SCI) is a new imaging method based on compressive sensing. It encodes image sequences into a single snapshot measurement and then recovers the original high-speed video through reconstruction algorithms, which has the advantages of a low hardware cost and high imaging efficiency. How to construct an efficient algorithm is the key problem of video SCI. Although the current mainstream deep convolution network reconstruction methods can directly learn the inverse reconstruction mapping, they still have shortcomings in the representation of the complex spatiotemporal content of video scenes and the modeling of long-range contextual correlation. The quality of reconstruction still needs to be improved. To solve this problem, we propose a Transformer-based Cascading Reconstruction Network for Video Snapshot Compressive Imaging. In terms of the long-range correlation matching in the Transformer, the proposed network can effectively capture the spatiotemporal correlation of video frames for reconstruction. Specifically, according to the residual measurement mechanism, the reconstruction network is configured as a cascade of two stages: overall structure reconstruction and incremental details reconstruction. In the first stage, a multi-scale Transformer module is designed to extract the long-range multi-scale spatiotemporal features and reconstruct the overall structure. The second stage takes the measurement of the first stage as the input and employs a dynamic fusion module to adaptively fuse the output features of the two stages so that the cascading network can effectively represent the content of complex video scenes and reconstruct more incremental details. Experiments on simulation and real datasets show that the proposed method can effectively improve the reconstruction accuracy, and ablation experiments also verify the validity of the constructed network modules. Full article

Photochemical internalisation (PCI) is a means of achieving spatio-temporal control of cytosolic drug delivery using sub-lethal photodynamic therapy (PDT), with a photosensitiser that can be activated by non-ionising visible light. Various 3D models including those developed at our laboratory, where spheroids are grown in a compressed collagen matrix, have been used for studying anti-cancer drug effects. However, the use of a more biomimetic tumouroid model which consists of a relatively hypoxic central cancer mass surrounded by its microenvironment (stroma) has not yet been explored in either toxicity or phototoxicity studies involving PCI. Here, we examined the efficacy of PCI using a porphyrin photosensitiser and a cytotoxin (Saporin) on ovarian cancer tumouroids, with HEY ovarian cancer cells in the central cancer compartment, and HDF fibroblast cells and HUVEC endothelial cells in the surrounding stromal compartment. The efficacy was compared to tumouroids treated with either Saporin or PDT alone, or no treatment. PCI treatment was shown to be effective in the tumouroids (determined through viability assays and imaging) and caused a considerable decrease in the viability of cancer cells both within the central cancer mass and those which had migrated into the stroma, as well as a reduction in the cell density of surrounding HUVEC and HDFs. Post-treatment, the mean distance of stromal invasion by cancer cells from the original cancer mass following treatment with Saporin alone was 730 μm vs. 125 μm for PCI. PDT was also effective at reducing viability in the central cancer mass and stroma but required a higher photosensitiser dose and light dose than PCI. Tumouroids, as tissue mimics, are suitable models for interrogating multicellular events following pharmacological assault. Full article

This paper follows up on a reference paper that inspired MDPI’s topic “Stochastic Geomechanics: From Experimentation to Forward Modeling”, in which the authors populated a spatio–temporal database of boundary displacement fields from a series of triaxial sand specimens using three-dimensional (3D) digital image correlation analysis. The database was curated and is currently available to the scientific community for further study. This paper uses a subset of this database, in which the experimental conditions were similar, to statistically investigate the dominant kinematic phenomena observed on the boundary of triaxial sand specimens under compression. The first-order 3D kinematic operators under the cylindrical coordinates, comprised of the divergence, curl, and gradient, were applied to the boundary displacement fields to illustrate the localization deformation patterns including the translational, rotational, shearing, and volumetric behaviors throughout the triaxial compression processes. Subsequently, the first-order statistics of the kinematic results are estimated, with the aim of revealing the evolution of associated localization effects as well as their corresponding uncertainties in space and time. The results of this research provide an innovative statistical interpretation of the localization effects on soil specimens under three-dimensional stress conditions. The proposed approach advances the interpretation of granular material’s responses under triaxial compression experimental conditions, while opening an opportunity to reproduce the material’s kinematic responses under the triaxial experimental conditions through constitutive modeling or machine learning techniques. Full article

The collapse of a cavitation bubble is always associated with the radiation of intense shock waves, which are highly relevant in a variety of applications. To radiate a strong shock wave, it is necessary to converge energy at the collapse, and understanding generation processes of multiple shock waves at the collapse is a key issue. In the present study, we investigated the formation of multiple shock waves generated by the collapse of a laser-induced bubble. We used a high-speed imaging system with unprecedented spatiotemporal resolution. We developed a triggering procedure of high precision and reproducibility based on the deflection of a laser beam by the shockwave passage. The high-speed videos clearly show that: (A) a first shockwave is emitted as the micro-jet hits the bottom of the bubble interface, followed by a second shock wave due to the collapse of the remaining toroidal bubble; (B) a sequential collapse of elongated bubbles, where the top part of the bubble collapses slightly before the bottom of the bubble; and (C) the formation of compression shock waves from multiple sites on a toroidal bubble. Full article