Target Image Research Articles

A Texture-Considerate Convolutional Neural Network Approach for Color Consistency in Remote Sensing Imagery

Remote sensing allows us to conduct large-scale scientific studies that require extensive mapping and the amalgamation of numerous images. However, owing to variations in radiation, atmospheric conditions, sensor perspectives, and land cover, significant color discrepancies often arise between different images, necessitating color consistency adjustments for effective image mosaicking and applications. Existing methods for color consistency adjustment in remote sensing images struggle with complex one-to-many nonlinear color-mapping relationships, often resulting in texture distortions. To address these challenges, this study proposes a convolutional neural network-based color consistency method for remote sensing cartography that considers both global and local color mapping and texture mapping constrained by the source domain. This method effectively handles complex color-mapping relationships while minimizing texture distortions in the target image. Comparative experiments on remote sensing images from different times, sensors, and resolutions demonstrated that our method achieved superior color consistency, preserved fine texture details, and provided visually appealing outcomes, assisting in generating large-area data products.

Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data

The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression with over 2000-fold compression ratio on 2D, 2D-T, 3D, 4D biomedical images of diverse targets ranging from single virus to entire human organs, and ensures reliable downstream tasks, such as object segmentation and quantitative analyses, to be conducted at high efficiency.

Gini Coefficient-Based Feature Learning for Unsupervised Cross-Domain Classification with Compact Polarimetric SAR Data

Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging geometry, weather conditions, and system parameters. Therefore, labeled samples in one image could not be suitable to represent the same target in other images. The domain distribution shift of different images reduces the reusability of the labeled samples. Thus, exploring cross-domain interpretation methods is of great potential for SAR images to improve the reuse rate of existing labels from historical images. In this study, an unsupervised cross-domain classification method is proposed that utilizes the Gini coefficient to rank the robust and stable polarimetric features in both the source and target domains (GRFST) such that an unsupervised domain adaptation (UDA) can be achieved. This method selects the optimal features from both the source and target domains to alleviate the domain distribution shift. Both fully polarimetric (FP) and compact polarimetric (CP) SAR features are explored for crop-domain terrain type classification. Specifically, the CP mode refers to the hybrid dual-pol mode with an arbitrary transmitting ellipse wave. This is the first attempt in the open literature to investigate the representing abilities of different CP modes for cross-domain terrain classification. Experiments are conducted from four aspects to demonstrate the performance of CP modes for cross-data, cross-scene, and cross-crop type classification. Results show that the GRFST-UDA method yields a classification accuracy of 2% to 12% higher than the traditional UDA methods. The degree of scene similarity has a certain impact on the accuracy of cross-domain crop classification. It was also found that when both the FP and circular CP SAR data are used, stable, promising results can be achieved.

Detection and Tracking of Low-Frame-Rate Water Surface Dynamic Multi-Target Based on the YOLOv7-DeepSORT Fusion Algorithm

This study aims to address the problem in tracking technology in which targeted cruising ships or submarines sailing near the water surface are tracked at low frame rates or with some frames missing in the video image, so that the tracked targets have a large gap between frames, leading to a decrease in tracking accuracy and inefficiency. Thus, in this study, we proposed a water surface dynamic multi-target tracking algorithm based on the fusion of YOLOv7 and DeepSORT. The algorithm first introduces the super-resolution reconstruction network. The network can eliminate the interference of clouds and waves in images to improve the quality of tracking target images and clarify the target characteristics in the image. Then, the shuffle attention module is introduced into YOLOv7 to enhance the feature extraction ability of the target features in the recognition network. Finally, Euclidean distance matching is introduced into the cascade matching of the DeepSORT algorithm to replace the distance matching of IOU to improve the target tracking accuracy. Simulation results showed that the algorithm proposed in this study has a good tracking effect, with an improvement of 9.4% in the improved YOLOv7 model relative to the mAP50-95 value and an improvement of 13.1% in the tracking accuracy in the DeepSORT tracking network compared with the SORT tracking accuracy.

Programming Fast DNA Amplifier Circuits with Versatile Toehold Exchange Pathway.

DNA amplifier circuits establish powerful tools to dynamically control molecular assembly for computation, sensing, and biological applications. However, the slow reaction speed remains a major barrier to their practical utility. Here, diverse fast DNA amplifier circuits termed toehold exchange polymerization (TEP) and toehold exchange catalysis (TEC) using toehold exchange-mediated assembly as a fundamental mechanism are built. Both TEP and TEC with a duplex and a hairpin can respond within minutes to diverse nucleic acid inputs with high fidelity. In addition, the circuits can amplify live-cell signals for fluorescence imaging target RNA dynamics and discriminating different cell lines. Compared with existing DNA circuits that involve time scales of hours for transducing small signals, TEP and TEC exhibit much faster dynamics, simpler design, and comparable sensitivity. These features make TEP and TEC promising platforms to develop programmable nucleic acid tools and devices and to create fast sensing and processing systems, amenable to wide practical applications.

Image Registration Algorithm for Stamping Process Monitoring Based on Improved Unsupervised Homography Estimation

Homography estimation is a crucial task in aligning template images with target images in stamping monitoring systems. To enhance the robustness and accuracy of homography estimation against random vibrations and lighting variations in stamping environments, this paper proposes an improved unsupervised homography estimation model. The model takes as input the channel-stacked template and target images and outputs the estimated homography matrix. First, a specialized deformable convolution module and Group Normalization (GN) layer are introduced to expand the receptive field and enhance the model’s ability to learn rotational invariance when processing large, high-resolution images. Next, a multi-scale, multi-stage unsupervised homography estimation network structure is constructed to improve the accuracy of homography estimation by refining the estimation through multiple stages, thereby enhancing the model’s resistance to scale variations. Finally, stamping monitoring image data is incorporated into the training through data fusion, with data augmentation techniques applied to randomly introduce various levels of perturbation, brightness, contrast, and filtering to improve the model’s robustness to complex changes in the stamping environment, making it more suitable for monitoring applications in this specific industrial context. Compared to traditional methods, this approach provides better homography matrix estimation when handling images with low texture, significant lighting variations, or large viewpoint changes. Compared to other deep-learning-based homography estimation methods, it reduces estimation errors and performs better on stamping monitoring images, while also offering broader applicability.

Differential Lucy-Richardson-Rosen algorithm for near diffraction-limited image restoration assisting by a wavefront sensor

Lucy-Richardson-Rosen algorithm (LR2A) is one of the most important tools for image restoration, especially in computer vision, target imaging and so on. However, the performance of the LR2A can be affected by large optical aberration and imaging noise and even failure under severe environment. To address the mentioned problem, we proposed a new Differential Lucy-Richardson-Rosen Algorithm(D-LR2A) to reduce the influence of optical aberrations and detection noise with advantages of faster speed of convergence and better restoration quality. In the modified optical configuration, a Hartmann-Shack wavefront sensor is employed to measure optical aberration and estimate accurately the Point Spread Function (PSF) of the optical imaging system. The numeric and experimental results show that the proposed method can be used to restore images with large optical aberration and worse noise level. Besides that, it also has faster speed, better restoration quality, and thus easier application as compared to those of LR2A

MMHCA: Multi-feature representations based on multi-scale hierarchical contextual aggregation for UAV-view geo-localization

In global navigation satellite system denial environment, cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle (UAV) systems. The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms, such as UAV-view and satellite-view images. However, images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform, view, and timing. The existing methods predominantly extract features by segmenting feature maps, which overlook the holistic semantic distribution and structural information of objects, resulting in loss of image information. To address these challenges, dilated neighborhood attention Transformer is employed as the feature extraction backbone, and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation (MMHCA) is proposed. In the proposed MMHCA method, the multi-scale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels, establishing feature associations of contextual information with global and local information in the image. Subsequently, the multi-feature representations method is utilized to obtain rich discriminative feature information, bolstering the robustness of model in scenarios characterized by positional shifts, varying distances, and scale ambiguities. Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques, showing outstanding results in UAV localization and navigation.

"One Method to Label Them All": A Single Fully Automated Protocol for GMP-Compliant 68Ga Radiolabeling of PSMA-11, Transposable to PSMA-I&T and PSMA-617.

Prostate-specific membrane antigen (PSMA) is an ideal target for molecular imaging and targeted radionuclide therapy in prostate cancer. Consequently, various PSMA ligands were developed. Some of these molecules are functionalized with a chelator that can host radiometals, such as 68Ga for PET imaging. The 68Ga radiolabeling step benefits from process automation, making it more robust and reducing radiation exposure. To design a single automated radiolabeling protocol for the GMP-compliant preparation of [68Ga]Ga-PSMA-11, transposable to the production of [68Ga]Ga-PSMA-617 and [68Ga]Ga-PSMA-I&T. A GAIA® synthesis module and a GALLIAD® generator were used. Radio-TLC and radio-HPLC methods were validated for radiochemical purity (RCP) determination. Three [68Ga]Ga-PSMA-11 validation batches were produced and thoroughly tested for appearance and pH, radionuclide identity and purity, RCP, stability, residual solvent and sterility. Minimal modifications were made to the reagents and disposables for optimal application to other PSMA ligands. [68Ga]Ga-PSMA-11 for clinical application was produced in 27 min. The 3 validation batches met the quality criteria expected by the European Pharmacopoeia to allow routine production. For optimal transposition to PSMA-617, the solid phase extraction cartridge was changed to improve purification of the radiolabeled product. For application to PSMA-I&T, the buffer solution initially used was replaced by HEPES 2.7 M to achieve good radiochemical yields. Residual HEPES content was checked in the final product and was below the Ph. Eur. threshold. A single automated radiolabeling method on the GAIA® module was developed and implemented for 68Ga radiolabeling of 3 PSMA ligands, with slight adjustments for each molecule.

Biomimetic copper-containing nanogels for imaging-guided tumor chemo-chemodynamic-immunotherapy

Developing multifunctional nanoplatforms to comprehensively modulate the tumor microenvironment and enhance diagnostic and therapeutic outcomes still remains a great challenge. Here, we report the facile construction of a multivariate nanoplatform based on cancer cell membrane (CM)-encapsulated redox-responsive poly(N-vinylcaprolactam) (PVCL) nanogels (NGs) co-loaded with Cu(II) and chemotherapeutic drug toyocamycin (Toy) for magnetic resonance (MR) imaging-guided combination tumor chemodynamic therapy/chemoimmunotherapy. We show that redox-responsive PVCL NGs formed through precipitation polymerization can be aminated, conjugated with 3,4-dihydroxyhydrocinnamic acid for Cu(II) complexation, physically loaded with Toy, and finally camouflaged with CMs. The created ADCT@CM NGs with an average size of 113.0 nm are stable under physiological conditions and can efficiently release Cu(II) and Toy under tumor microenvironment with a high level of glutathione. Meanwhile, the developed NGs are able to enhance cancer cell oxidative stress and endoplasmic reticulum stress by synergizing the effects of chemodynamic therapy mediated by Cu-based Fenton-like reaction and Toy-mediated chemotherapy, thereby triggering significant immunogenic cell death (ICD). In a melanoma mouse model, the NGs show potent immune activation effects to reinforce tumor therapeutic efficacy through ICD induction and immune modulation including high levels of immune cytokine secretion, increased tumor infiltration of CD8+ cytotoxic T cells, and reduced tumor infiltration of regulatory T cells. With the CM coating and Cu(II) loading, the developed NG platform demonstrates homologous tumor targeting and T1-weighted MR imaging, hence providing a general biomimetic NG platform for ICD-facilitated tumor theranostic nanoplatform. Statement of significanceDeveloping multifunctional nanoplatforms to comprehensively modulate the tumor microenvironment (TME) and enhance theranostic outcomes remains a challenge. Here, a cancer cell membrane (CM)-camouflaged nanoplatform based on aminated poly(N-vinylcaprolactam) nanogels (NGs) co-loaded with Cu(II) and toyocamycin (Toy) was prepared for magnetic resonance (MR) imaging-guided combination tumor chemodynamic therapy/chemoimmunotherapy. The tumor targeting specificity and efficient TME-triggered release of Cu(II) and Toy could enhance tumor cell oxidative stress and endoplasmic reticulum stress by synergizing the effects of chemodynamic therapy mediated by Cu-based Fenton-like reaction and Toy-mediated chemotherapy, respectively, thereby leading to significant immunogenic cell death (ICD) and immune response. With the CM coating and Cu(II) loading, the developed NG platform also demonstrates good T1-weighted tumor MR imaging performance. Hence, this study provides a general biomimetic NG platform for ICD-facilitated tumor theranostics.

A Distorted Light Field Image Correction Method Based on Improved Hough Transform

Introduction: In using a camera to take photos, due to environmental limitations, uneven lighting can cause uneven distribution of the image light field, resulting in distortion of the image background and target, blurring of details, and distorted light field images. Method: In view of this, research is conducted on the correction of distorted light field images based on the Hough transform. First, the improved Hough transform is utilized to locate the four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets the correction result of the distorted light field image. Result: The research results indicate that in the practical application of the distorted light field image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one. Conclusion: In comparative experiments, the research algorithm outperforms the other three algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of 54.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction results.

Infrared Weak and Small Target Detection Algorithm Based on Deep Learning

Abstract In the infrared imaging scene where the target is at a long distance and the background is cluttered, due to the interference of noise and background texture information, the infrared image is prone to problems such as low contrast between the target and the background, and feature confusion, which makes it difficult to accurately extract and detect the target. To solve this problem, firstly, the infrared image is enhanced by combining DDE and MSR algorithm to improve the contrast and detail visibility of the image. For the RT-DETR network structure, the EMA attention mechanism is introduced into the backbone to enhance the feature extraction ability of the model by extracting context information. The CAMixing convolutional attention module is introduced into CCFM, and the multi-scale convolutional self-attention mechanism is introduced to focus on local information and enhance the detection ability of small targets. The filtering rules of the prediction box are improved, combined with Shape-IoU, and the convergence speed of the loss function in the detection and the detection accuracy of small targets are improved by paying attention to the influence of the intrinsic properties of the bounding box itself on the regression. In the experiment, the infrared weak target image dataset of the National University of Defense Technology was selected, labeled and trained. Experimental results show that compared with the original DETR algorithm, the average precision of the improved algorithm (mAP) is increased by 3.2%, and it can effectively detect infrared weak and small targets in different complex backgrounds, which reflects good robustness and adaptability, and can be effectively applied to infrared weak and small target detection in complex backgrounds.

High‐resolution incoherent interference imaging without phase measurement

AbstractAiming for long‐distance, high‐resolution, passive imaging, we use fiber coupling to replace the spatial coupling of the segmented planar imaging detector for electro‐optical reconnaissance imaging system and propose a novel incoherent interference imaging method, which avoids the problem that the resolution of the imaging system is limited by the size of the silicon wafer. The method uses a phase retrieval algorithm and does not need to measure the phase in the system, effectively avoiding the intrinsic jitter problem of fiber optics. The feasibility of the method in achieving target image reconstruction was verified through simulation; furthermore, an indoor two‐dimensional discrete sampling imaging experiment for a simple four‐rod target was conducted, and an outdoor one‐dimensional feature identification experiment for a two‐rod target was also performed. The results showed that the resolution exceeds the diffraction limit and the reconstruction error of target size is less than 3.5%.

Medium-adaptive compressive diffuse optical tomography.

The low spatial resolution of diffuse optical tomography (DOT) has motivated the development of high-density DOT systems utilizing spatially-encoded illumination and detection strategies. Data compression methods, through the application of Fourier or Hadamard patterns, have been commonly explored for both illumination and detection but were largely limited to pre-determined patterns regardless of imaging targets. Here, we show that target-optimized detection patterns can yield significantly improved DOT reconstructions in both in silico and experimental tests. Applying reciprocity, we can further iteratively optimize both illumination and detection patterns and show that these simultaneously optimized source/detection patterns outperform predetermined patterns in simulation settings. In addition, we show media-adaptive measurement data compression methods enable wide-field DOT systems to recover highly complex inclusions inside optically-thick media with reduced background artifacts. Furthermore, using truncated optimized patterns shows an improvement of 2-4× in increased speed of data acquisition and reconstruction without significantly losing image quality. The proposed method can be readily extended for additional data dimensions such as spectrum and time.

CYP7B1 deficiency impairs myeloid cell activation in autoimmune disease of the central nervous system.

Dysregulation of cholesterol metabolism underlies neurodegenerative disease and is increasingly implicated in neuroinflammatory diseases, such as multiple sclerosis (MS). Cytochrome P450 family 7 subfamily B member 1 (CYP7B1) is a key enzyme in alternative cholesterol metabolism. A recessive mutation in the gene CYP7B1 is known to cause a neurodegenerative disease, hereditary spastic paraplegia type 5 and oxysterol accumulation. However, the role of CYP7B1 in neuroinflammation has been little revealed. In this study, we induced experimental autoimmune encephalomyelitis (EAE), as a murine model of MS, using CYP7B1 homozygous knockout (KO) mice. We found that CYP7B1 deficiency can significantly attenuate EAE severity. CYP7B1 deficiency is sufficient to reduce leukocyte infiltration into the central nervous system, suppress proliferation of pathogenic CD4+ T cells, and decrease myeloid cell activation during EAE. Additionally, live-animal imaging targeting translocator protein expression, an outer mitochondrial membrane protein biomarker of neuroinflammation, showed that CYP7B1 deficiency results in suppressed neuroinflammation. Using human monocyte-derived microglia-like cellular disease model and primary microglia of CYP7B1 KO mice, we also found that activation of microglia of CYP7B1 deficiency was impaired. These cumulative results suggest that CYP7B1 can regulate neuroinflammation, thus providing potential new targets for therapeutic intervention.

Deep-UEO: Empowering medical image registration with hybrid strategy based on deep learning and united equilibrium optimizer

Traditional optimization methods encounter challenges in determining the optimal geometric transformation parameters and often lacking to achieve global optimum which leads to extended computational time required for convergence. To address this challenge, a dual-phase strategy is proposed for multimodal biomedical image registration by amalgamating the united equilibrium optimizer (UEO) with Visual Geometry Group (VGG-19). In first phase, the input images are aligned by identifying the optimal values of rigid transformation parameters through UEO, with mutual information (MI) maximization serving as an objective function. In second phase, VGG19 is employed to enhance the reliability of resulting registered image obtained through UEO optimization for extracting both the low- and high-level features of reference and target images. Following this, dynamic inlier selection is employed to refine the matching process, integrating expectation maximization for iterative updating of inlier selection, thus improving registration accuracy. Finally, thin plate spline interpolation is utilized to calculate the transformation matrix, ensuring precise alignment between reference and UEO registered image. Extensive experiments are conducted on multimodal medical images sourced from publicly available repositories and clinical(in-house) datasets to validate the reliability of the proposed scheme. These experiments showcase substantial improvements in RMSE from 22.21094 to 10.983654, SSIM from 0.7190832 to 0.8409321, PSNR from 30.176044 to 34.095471, and CC from 0.9304633 to 0.9809432, outperforming state-of-the-art methods.

ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning

Traditional range–instantaneous Doppler (RID) methods for maneuvering target imaging are hindered by issues related to low resolution and inadequate noise suppression. To address this, we propose a novel ISAR imaging method enhanced by deep learning, which incorporates the fundamental architecture of CapsNet along with two additional convolutional layers. Pre-training is conducted through the deep learning network to establish the mapping function for reference. Subsequently, the trained network is integrated into the electromagnetic simulation software, Feko 2019, utilizing a combination of geometric forms such as corner reflectors and Luneberg spheres for analysis. The results indicate that the derived ISAR imaging effectively identifies the ISAR program associated with complex aerial targets. A thorough analysis of the imaging results further corroborates the effectiveness and superiority of this approach. Both simulation and empirical data demonstrate that this method significantly enhances imaging resolution and noise suppression.

SeACPFusion: An Adaptive Fusion Network for Infrared and Visible Images based on brightness perception

Generating a single fused image that highlights important targets and preserves textural details is the aim of fusing visible and infrared images. The majority of deep learning-based fusion algorithms now in use can produce decent fusion outcomes; however, the modeling process still lacks consideration of the different amounts of information in different scenes or regions. Thus, we propose in this research SeACPFusion, a luminance-aware adaptive fusion network for infrared and visible images, which adaptively preserves the intensity information of the noticeable targets of the source images with the texture information of the background in an optimal ratio. Specifically, we design pixel-level luminance loss (PBL) to direct the fusion model’s training in real-time, and PBL retains the optimal intensity information according to the pixel luminance ratio of different source images. In addition, we designed the Channel Transformer (CTF) to consider the relationship between different attributes from the point of view of the feature channel and to focus on the key information by using the self-focusing mechanism to achieve the goal of adaptive fusion. Our extensive tests on the MSRS, RoadScene, and TNO datasets demonstrate that SeACPFusion surpasses nine representative deep learning methods on six objective metrics and achieves the best visual results in scenes such as overexposure or underexposure. In addition, the relatively efficient operation and fewer model parameters make our algorithm promising as a preprocessing module for downstream complicated vision tasks.

A novel data-driven framework for enhancing the consistency of deposition contours and mechanical properties in metal additive manufacturing

The accuracy and quality of part formation are crucial considerations. However, the laser directed energy deposition (L-DED) process often leads to irregular changes in deposition contours and mechanical properties across parts due to complex flow fields and temperature variations. Hence, to ensure the forming accuracy and quality, it is necessary to achieve precise monitoring and appropriate parameter adjustments during the processing. In this study, a machine vision method for real-time monitoring is proposed, which combines target tracking and image processing techniques to achieve accurate recognition of deposition contours under noisy conditions. Through comparative verification, the measurement accuracy reaches as high as 98.98 %. Leveraging the monitoring information, a bidirectional prediction neural network is proposed to accomplish layer-by-layer forward prediction of layer height. Meanwhile, inverse prediction is employed to determine the processing parameters required for achieving the desired layer height, facilitating the optimization of the deposition contours. It was found that as the processing parameters were adjusted layer-by-layer to achieve consistent deposition contours, there was also a tendency towards consistent changes in microstructure and mechanical properties. The standard deviations of primary dendrite arm spacing (PDAS) and ultimate tensile strength (UTS) at different positions decrease by over 52.2 % and 61.4 %, respectively. This study reveals the consistent patterns of variation in deposition contours and mechanical properties under data-driven variable parameter processing, laying an important foundation for future exploration of the complex process-structure-performance (PSP) relationship in L-DED.

Development of Polarization-Insensitive THz-to-IR Converters for Low-IR-Signature Target Detection and Imaging.

A THz-to-IR converter can be an effective solution for the detection of low-IR-signature targets by combining the advantages of mature IR detection mechanisms with high atmospheric transmittance in the THz region. A metallic metasurface (MS)-based absorber with linear polarization dependence based on a split-ring resonator (SRR) unit cell has been previously studied as a preliminary example of a THz-to-IR converter structure in the literature. In this simulation-based study, a new cross-shaped unit cell-based metallic MS absorber structure sensitive to dual polarization is designed to eliminate linear polarization dependency, thereby allowing for incoherent detection of THz radiation. A model is developed to calculate the temperature difference and the response time for this new cross-shaped absorber structure, and its performance is compared to the SRR structure for both coherent and incoherent illumination. This model allows for understanding the efficiency of these structures by considering all loss mechanisms which previously had not been considered. It is found that both structures show similar performance under linearly polarized coherent illumination. However, under incoherent illumination, the IR emittance efficiency as gauged by the temperature difference for the cross-shaped structure is found to be twice as high as compared to the SRR structure. The results also imply that calculated temperature differences for both structures under coherent and incoherent illumination are well above the limit of the minimum resolvable temperature difference of the state-of-the-art IR cameras. Therefore, dual-polarized or multi-polarization-sensitive MS absorber structures can be crucial for developing cost-effective THz-to-IR converters and be implemented in THz imaging solutions.