Spatial Image Research Articles

Nanoplastics Detected in Commercial Sea Salt.

People of all ages consume salt every day, but is it really just salt? Plastic nanoparticles [nanoplastics (NPs)] pose an increasing environmental threat and have begun to contaminate everyday salt in consumer goods. Herein, we developed a combined surface enhanced Raman scattering (SERS) and stimulated Raman scattering (SRS) approach that can realize the filtration, enrichment, and detection of NPs in commercial salt. The Au-loaded (50 nm) anodic alumina oxide substrate was used as the SERS substrate to explore the potential types of NP contaminants in salts. SRS was used to conduct imaging and quantify the presence of the NPs. SRS detection was successfully established through standard plastics, and NPs were identified through the match of the hydrocarbon group of the nanoparticles. Simultaneously, the NPs were quantified based on the high spatial resolution and rapid imaging of the SRS imaging platform. NPs in sea salts produced in Asia, Australasia, Europe, and the Atlantic were studied. We estimate that, depending on the location, an average person could be ingesting as many as 6 million NPs per year through the consumption of sea salt alone. The potential health hazards associated with NP ingestion should not be underestimated.

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R2 = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R2 as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP.

Abstract PS03-03: The spatially resolved single-cell atlas of the tumor immune architecture revealed the central role of IFN-alpha and plasmacytoid dendritic cells in triple-negative breast cancer in the Mayo Clinic cohort and FinXX trial

Abstract Background: Multiple studies have confirmed the central role of preexisting immune response measured by stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative breast cancer (TNBC). Emerging studies showed that not only the number of TILs but also the location of TILs is important. There are 3 distinct immune architectures described based on the amount and locations of TILs, namely immune enriched (IN), immune excluded (IE), and immune desert (ID). Here we evaluated outcomes and characteristics associated with each immune landscape. Methods: NanoString Digital Spatial Profiling (DSP) and CosMx, a spatial multi-omics single-cell imaging platform, were performed in 75 samples from the Mayo Clinic (MC) TNBC cohort (Leon-Ferre BCRT 2018). NanoString IO360 was performed in 114 samples from the FinXX trial ( NCT00114816) . Firstly, tumors with sTIL quantified by H&E ≤ 30% were classified as ID. The rest of the tumors with high sTIL > 30% were categorized according to the intratumoral CD8 protein expression by DSP, with IE having intratumoral CD8 in the lower median and IN having intratumoral CD8 in the upper median. Chi-square test, gene set enrichment, Cox regression, and Kaplan-Meier analysis were used. Differential expression listed as log 2-fold change (FC) was estimated from the linear mixed model with significance defined as two-sided p< 0.05. Results: ID is associated with low Ki67 < 5% (23.3% vs. 9.6% ID, p 0.02) as well as apocrine (11/13, 84.6%) and metaplastic histology (10/12, 83%). In both univariate and multivariate analysis, patients with IN had significantly improved recurrence-free survival (RFS) compared to those with ID (HR 0.37, 95%CI 0.18-0.74, p 0.005). Despite having high sTILs, IE had poor outcomes similar to ID (HR 0.84, 95%CI 0.36-1.98). Strikingly, we identified that IE patients had significantly lower plasmacytoid dendritic cells (pDCs) compared to IN (mean 0 vs. 0.26/100 tumor cells, 95%CI 0.08-0.43 , p 0.01). Using Gene Set Enrichment Analysis to evaluate differential hallmarks between IN and IE, we identified IF Nɑ and IFNγ (FDR < 0.001) responses as significantly enriched in IN group, consistent wi th the function of pDCs, which are a subset of dendritic cells specialized in secreting high levels of type I interferon. To validate this finding, we further evaluated the 11 leading edge gene IFNɑ signature in the FinXX trial. A high IFNɑ signature score was associated with significantly improved outcomes in the FinXX trial (HR 0.21, 95%CI 0.09-0.51 , p < 0.001). Similar findings were observed using Kaplan-Meier analysis in the FinXX trial with significantly improved RFS (p 0.0006) and overall survival (p 0.0001) in patients with high IFNɑ signature scores. Furthermore, we evaluated the differential gene expression unique to IN tumors in the Mayo cohort. Expressions of MHC class I and class II in tumor cells, including HLA-A, HLA-B, HLA-C, HLA-DRA, HLA-DRB1, HLA-DPA1, and HLA-E, were associated with IN and significantly improved outcomes (p < 0.05) compared to ID and IE. Conclusions: Highlighting the importance of spatial context, we identified that patients with IE tumors had poor outcomes despite having high TILs. Moreover, u sing an in-depth analysis with spatially defined context, we identified the central role of pDC and the significance of IF Nɑ in TNBC. Support: Breast Cancer Research Foundation, Mayo Clinic Breast Cancer SPORE (P50CA116201-17) W81XWH-15-1-0292, P50CA015083, R35CA253187 Citation Format: Saranya Chumsri, Yi Liu, Yaohua Ma, Jodi Carter, Mark Gregory, Sarah Church, Jason Reeves, Heather Ann Brauer, Sarah Warren, Heikki Joensuu, Edith Perez, Roberto Leon-Ferre, David Hillman, Judy Boughey, James Ingle, Krishna Kalari, Fergus Couch, Matthew Goetz, Keith Knutson, E. Thompson. The spatially resolved single-cell atlas of the tumor immune architecture revealed the central role of IFN-alpha and plasmacytoid dendritic cells in triple-negative breast cancer in the Mayo Clinic cohort and FinXX trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-03.

Multilayer lensless camera for improving the condition number

Computational lensless imaging technology implements the thinning of the optical system by replacing optical imaging with image reconstruction processing. The conventional optical design uses a single coded mask and an image sensor; however, researchers have recently proposed optical designs incorporating multiple stacked coded apertures for multidimensional and wide-field imaging. Here, we investigate the effects of multilayering the coded aperture on the performance of two-dimensional spatial imaging. Through simulations and optical experiments, we demonstrate that multilayering the coded aperture enhances the condition number of the optical system’s transmission matrix and consequently improves the accuracy of image reconstruction in lensless imaging.

The anterior cingulate cortex is involved in intero-exteroceptive integration for spatial image transformation of the self-body.

Spatial image transformation of the self-body is a fundamental function of visual perspective-taking. Recent research underscores the significance of intero-exteroceptive information integration to construct representations of our embodied self. This raises the intriguing hypothesis that interoceptive processing might be involved in the spatial image transformation of the self-body. To test this hypothesis, the present study used functional magnetic resonance imaging to measure brain activity during an arm laterality judgment (ALJ) task. In this task, participants were tasked with discerning whether the outstretched arm of a human figure, viewed from the front or back, was the right or left hand. The reaction times for the ALJ task proved longer when the stimulus presented orientations of 0°, 90°, and 270° relative to the upright orientation, and when the front view was presented rather than the back view. Reflecting the increased reaction time, increased brain activity was manifested in a cluster centered on the dorsal anterior cingulate cortex (ACC), suggesting that the activation reflects the involvement of an embodied simulation in ALJ. Furthermore, this cluster of brain activity exhibited overlap with regions where the difference in activation between the front and back views positively correlated with the participants' interoceptive sensitivity, as assessed through the heartbeat discrimination task, within the pregenual ACC. These results suggest that the ACC plays an important role in integrating intero-exteroceptive cues to spatially transform the image of our self-body.

Fully dense generative adversarial network for removing artifacts caused by microwave dielectric effect in thermoacoustic imaging

Microwave-induced thermoacoustic (TA) imaging (MTAI) combines pulsed microwave excitation and ultrasound detection to provide high contrast and spatial resolution images through dielectric contrast, which holds great promise for clinical applications. However, artifacts caused by microwave dielectric effect will seriously affect the accuracy of MTAI images that will hinder the clinical translation of MTAI. In this work, we propose a deep learning-based method fully dense generative adversarial network (FD-GAN) for removing artifacts caused by microwave dielectric effect in MTAI. FD-GAN adds the fully dense block to the generative adversarial network (GAN) based on the mutual confrontation between generator and discriminator, which enables it to learn both local and global features related to the removal of artifacts and generate high-quality images. The practical feasibility was tested in simulated, experimental data. The results demonstrate that FD-GAN can effectively remove the artifacts caused by the microwave dielectric effect, and shows superiority in denoising, background suppression, and improvement of image distortion. Our approach is expected to significantly improve the accuracy and quality of MTAI images, thereby enhancing the diagnostic accuracy of this innovative imaging technique.

Ultra-High Spatial Resolution Images of the Temporal Bone Obtained With a Newly Released Photon-Counting Detector Computed Tomography.

Ultra-High Spatial Resolution Images of the Temporal Bone Obtained With a Newly Released Photon-Counting Detector Computed Tomography.

Unsupervised multi-level spatio-spectral fusion transformer for hyperspectral image super-resolution

Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) is widely used for HSI super-resolution. Recent works still face problems in exploring global spatio-spectral correlation and lack effective utilization of multi-level features of the inputs (i.e., HR-MSI and LR-HSI), which results in a lack of similarity between the reconstruction and the inputs, ultimately causing significant spatio-spectral distortion. To solve these problems, we design an Unsupervised Multi-level Spatio-spectral Fusion Transformer (UMSFT). In UMSFT, a novel multi-level features fusion strategy is constructed, which fuses the hierarchy features of the inputs via proposed Spatio-spectral Feature Fusion Attention Blocks (S2F-FAB) in a level-by-level manner, thereby fully exploring the interaction between the hierarchical features. The S2F-FAB is specially designed for HSI-MSI fusion consisting of two components: (1) a spatial fusion module (Spa-FM) is designed for spatial domain fusion, and its output is set as Values (V) of a transformer; (2) a novel spectral feature cross attention (Spe- FCA) formulates the features of LR-HSI and HR-MSI as Queries (Q) and Keys (K), respectively, and achieves spectral domain fusion by applying attention mechanism along the spectral dimension. Incorporating spatial detail reconstruction and spectral feature integration into the attention mechanism, the S2F-FAB efficiently exploits the spatio-spectral correlation between target HR-HSI and inputs. Experimental results on three public datasets and our real-world images show the superiority of our method as compared with eleven state-of-the-art methods. Codes will be available at https://github.com/Caoxuheng/HIFtool.

Human NLRC4 promotes cancer survival and is associated to type-I interferon signaling and immune infiltration.

The immune system can control cancer progression. However, even though some innate immune sensors of cellular stress are expressed intrinsically in epithelial cells, their potential role in cancer aggressiveness and subsequent overall survival in humans is mainly unknown. Here, we show that NLR family CARD Domain Containing 4 (NLRC4) is downregulated in epithelial tumor cells of colorectal cancer (CRC) patients by using spatial tissue imaging. Strikingly, only the loss of tumor NLRC4 but not stromal is associated with poor immune infiltration (mainly dendritic and CD4+/CD8+ T cells) and accurately predicts progression to metastatic Stage IV and decrease of overall survival. By combining multi-omics approaches, we show that restoring NLRC4 expression in human colorectal cancer cells triggers a broad inflammasome-independent immune reprogramming consisting of Type-I IFN signaling genes and the release of chemokines and myeloid growth factors involved in the tumor infiltration and activation of dendritic cells (DCs) and T cells. Consistently, such reprogramming in cancer cells is sufficient to directly mature human DCs towards a Th1 antitumor immune response through IL-12 production in vitro. In multiple human carcinomas (colorectal, lung, and skin), we confirmed that NLRC4 expression in patient tumors is strongly associated with Type-I IFN genes, immune infiltrates and high microsatellite instability. Thus, we shed light on the epithelial innate immune sensor NLRC4 as a novel therapeutic target to promote an efficient antitumor immune response against the aggressiveness of various carcinomas.

High spatial resolution surface plasmon resonance imaging using a plasmonic chip.

The surface plasmon resonance (SPR) technique has been widely applied to biosensing technologies for the rapid quantification of biomolecules without enzyme and fluorescent labeling. However, the conventional prism-coupling SPR method generally has a detection area of a few mm2, and the large contribution of the background signal forms a barrier to highly sensitive detection. Based on a highly spatially resolved SPR method, the present study constructed a scanning GC-SPR imaging instrument using an objective lens with a high numerical aperture and a plasmonic chip that could be used for grating-coupled SPR. Focusing light on the diffraction limit can suppress background signals and improve detection sensitivity. SPR imaging can also be performed by scanning a focal spot. Using this method, the refractive index of a mixture of water and dimethyl sulfoxide was measured with a detection accuracy of 2.43 × 10-3 RIU. Polydopamine films prepared with a thickness of <5nm were also measured, and each film thickness was evaluated with high sensitivity from the effective refractive index detected in a small area of <1 µm2.

Multi-frequency spatial frequency domain imaging: a depth-resolved optical scattering model to isolate scattering contrast in thin layers of skin.

Current methods for wound healing assessment rely on visual inspection, which gives qualitative information. Optical methods allow for quantitative non-invasive measurements of optical properties relevant to wound healing. Spatial frequency domain imaging (SFDI) measures the absorption and reduced scattering coefficients of tissue. Typically, SFDI assumes homogeneous tissue; however, layered structures are present in skin. We evaluate a multi-frequency approach to process SFDI data that estimates depth-specific scattering over differing penetration depths. Multi-layer phantoms were manufactured to mimic wound healing scattering contrast in depth. An SFDI device imaged these phantoms and data were processed according to our multi-frequency approach. The depth sensitive data were then compared with a two-layer scattering model based on light fluence. The measured scattering from the phantoms changed with spatial frequency as our two-layer model predicted. The performance of two models solutions for SFDI was consistently better than the standard diffusion approximation. We presented an approach to process SFDI data that returns depth-resolved scattering contrast. This method allows for the implementation of layered optical models that more accurately represent physiologic parameters in thin tissue structures as in wound healing.

Relationship between monthly surface water derived from Sentinel-2 imagery and meteorological data (precipitation and evaporation) at Baghdad, Iraq

Abstract This study was carried out using two main monthly datasets: satellite images and some meteorological parameters (like precipitation and evaporation) for 2 years (2018 and 2021). Using the fine spatial resolution images acquired from Sentinel-2, digital maps of surface water areas were produced by calculating the Modified Normalized Difference Water Index for all months in QGIS and then classifying them into two main categories (water and non-water bodies). The results show that the largest areas were in January 2021 (77.2 km2) and 40.7 km2 in November 2018, while the lowest (18.7 km2) were in June 2021 and July 2018. Monthly mean values of cumulative precipitation and evaporation were used to find their relationships with surface water areas. The results show that the year 2021 was a severe dry year, whereas the lowest precipitation (25 mm) in that year was associated with higher evaporation losses (3,021.8 mm) during this year. When combined with surface water areas and meteorological parameters, a non-linear relationship between water areas and evaporation was found for both years, and a linear relationship between precipitation and water areas for the wet year of 2018.

Canopy-Level Spectral Variation and Classification of Diverse Crop Species with Fine Spatial Resolution Imaging Spectroscopy

Canopy-Level Spectral Variation and Classification of Diverse Crop Species with Fine Spatial Resolution Imaging Spectroscopy

Frequency-domain image encryption based on IWT and 3D S-box

Most of the existing spatial domain image encryption techniques suffer from the difficulty of resisting cryptographic attacks. For this reason, this paper proposes a frequency-domain based digital image encryption scheme by combining Integer Wavelet Transform (IWT), three-dimensional S-box and chaotic system. First, the plaintext image is decomposed into different frequency subbands by IWT to map the digital image from spatial domain to frequency domain. Second, the plaintext hash value is selected as the dynamic key, and dynamic chaotic pseudo-random sequences with associations are generated, which are used for the encryption of each module respectively. Then, a three-dimensional S-box is designed to encrypt the information-rich low-frequency information using ‘bit-permutation three-dimensional S-box replace ciphertext interleaved diffusion’, while the high-frequency information is encrypted using a lightweight ‘XOR-row column permutation’ operation. Finally, the secure ciphertext for public channel transmission is obtained by the reconstruction method. The scheme of this paper, the frequency domain transformation is implemented through IWT, which enhances the ability to resist attacks. In addition, the diffusion encryption modules employ the introduction of ciphertext interleaved diffusion and parallel encryption mechanisms, thus the algorithm has the ability to resist plaintext attacks. Theoretical analysis and empirical results show that the algorithm has excellent numerical statistical analysis results, which corroborate that it has good confusion, diffusion and avalanche effects, and is able to resist various common cryptographic attacks. The frequency domain image encryption scheme proposed in this paper is a preferred high-security digital image privacy protection technique, so it has good application prospects.

The AstraLux-TESS high spatial resolution imaging survey. Search for stellar companions of 215 planet candidates from TESS

Chance-aligned sources or blended companions can cause false positives in planetary transit detections or simply bias the determination of the candidate properties. In the era of high-precision space-based photometers, the need for high spatial resolution images has been demonstrated to be critical for validating and confirming transit signals. This already applied to the Kepler mission, is now applicable to the TESS survey, and will be critical for the PLATO mission. In this paper we present the results of the AstraLux-TESS survey, a catalog of high spatial resolution images obtained with the AstraLux instrument at the Calar Alto observatory (Almer\'ia, Spain) in the context of the TESS Follow-up Observing Program. We used the lucky imaging technique to obtain high spatial resolution images from planet candidate hosts included mostly in two relevant regimes: exoplanet candidates belonging to the level one requirement of the TESS mission (planets with radii $R&lt;4 R_ oplus $) and TESS planet candidates around intermediate-mass main-sequence stars. Among the 185 planet host candidate stars observed, we found 13 (7&lt;!PCT!&gt;) to be accompanied by additional sources within a separation of 2.2 arcsec . Among them, six are not associated with sources in the DR3 catalog, thus contaminating the TESS light curve. Even if no contaminants have been detected, we can provide upper limits and probabilities to the possible existence of field contaminants through the sensitivity limits of our images. Among the isolated hosts, we can discard hazardous companions (bright enough to mimic a planetary transit signals) with an accuracy below 1&lt;!PCT!&gt; for all their planets. The results from this catalog are key to the statistical validation of small planets (prime targets of the TESS mission) and planets around intermediate-mass stars in the main sequence. These two populations of planets are difficult to confirm with the radial velocity technique because of the shallow amplitude of small planets and the high rotational velocities and low number of available spectral lines in the intermediate stellar mass regime. Our results also demonstrate the importance of this type of follow-up observation for future transit missions such as PLATO, even in the era.

High Spatial Resolution 2D Imaging of Current Density and Pressure for Graphene Devices under High Pressure Using Nitrogen-Vacancy Centers in Diamond.

Current density imaging is helpful for discovering interesting electronic phenomena and understanding carrier dynamics, and by combining pressure distributions, several pressure-induced novel physics may be comprehended. In this work, noninvasive, high-resolution two-dimensional images of the current density and pressure gradient for graphene ribbon and hBN-graphene-hBN devices are explored using nitrogen-vacancy (NV) centers in diamond under high pressure. The two-dimensional vector current density is reconstructed by the vector magnetic field mapped by the near-surface NV center layer in the diamond. The current density images accurately and clearly reproduce the complicated structure and current flow of graphene under high pressure. Additionally, the spatial distribution of the pressure is simultaneously mapped, rationalizing the nonuniformity of the current density under high pressure. The current method opens a significant new avenue to investigate electronic transport and conductance variations in two-dimensional materials and electrical devices under high pressure as well as for nondestructive evaluation of semiconductor circuits.

Holoscopic Elemental-Image-Based Disparity Estimation Using Multi-Scale, Multi-Window Semi-Global Block Matching

In Holoscopic imaging, a single aperture is used to acquire full-colour spatial images like a fly’s eye by gently altering angles between nearby lenses with a micro-lens array. Due to its simple data collection and visualisation methods, which provide robust and scalable spatial information, and its motion parallax, binocular disparity, and convergence, this technique may be able to overcome traditional 2D imaging issues like depth, scalability, and multi-perspective problems. A novel disparity-map-generating method uses angular information from a single Holoscopic image’s micro-images, or Elemental Images (EIs), to create a scene’s disparity map. Not much research has used EIs instead of Viewpoint Images (VPIs) for disparity estimation. This study investigates whether angular perspective data may replace spatial orthographic data. Using noise reduction and contrast enhancement, EIs with a low resolution and lack of texture are pre-processed to calculate the disparity. The Semi-Global Block Matching (SGBM) technique is used to calculate the disparity between EI pixels. A multi-resolution approach overcomes EIs’ resolution constraints, and a content-aware analysis dynamically modifies the SGBM window size settings to generate disparities across different texture and complexity levels. A background mask and nearby EIs with accurate backgrounds detect and rectify EIs with erroneous backgrounds. Our method generates disparity maps that outperform two state-of-the-art deep learning algorithms and VPIs in real images.

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

Dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging

Temporal compressive coherent diffraction imaging is a lensless imaging technique with the capability to capture fast-moving small objects. However, the accuracy of imaging reconstruction is often hindered by the loss of frequency domain information, a critical factor limiting the quality of the reconstructed images. To improve the quality of these reconstructed images, a method dual-domain mean-reverting diffusion model-enhanced temporal compressive coherent diffraction imaging (DMDTC) has been introduced. DMDTC leverages the mean-reverting diffusion model to acquire prior information in both frequency and spatial domain through sample learning. The frequency domain mean-reverting diffusion model is employed to recover missing information, while hybrid input-output algorithm is carried out to reconstruct the spatial domain image. The spatial domain mean-reverting diffusion model is utilized for denoising and image restoration. DMDTC has demonstrated a significant enhancement in the quality of the reconstructed images. The results indicate that the structural similarity and peak signal-to-noise ratio of images reconstructed by DMDTC surpass those obtained through conventional methods. DMDTC enables high temporal frame rates and high spatial resolution in coherent diffraction imaging.

Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images

Refinement analysis of landslide risk assessment for wide area based on UAV-acquired high spatial resolution images