Infrared Physics & Technology最新文献

{"title":"C2F-AFE: A coarse-to-fine infrared and visible image registration method based on aggregation feature extraction","authors":"Chongtao Qiu ,&nbsp;Qimin Yang ,&nbsp;Kan Ren,&nbsp;Qian Chen","doi":"10.1016/j.infrared.2025.106199","DOIUrl":"10.1016/j.infrared.2025.106199","url":null,"abstract":"<div><div>In recent years, infrared and visible image registration has advanced rapidly due to the wide application of infrared and visible sensor vision systems. However, existing methods remain susceptible to nonlinear intensity differences (NID) and scale differences, while commonly suffering from inadequate feature extraction, low repeatability, and inefficient feature utilization. To address these limitations, we propose a coarse-to-fine infrared and visible image registration method based on aggregation feature extraction (C2F-AFE). First, we develop an aggregation feature extraction method based on maximum phase map and weighted moment map to obtain more repeatable feature points. Second, we construct a projection scale space for infrared images to achieve scale invariance. Third, we design a feature descriptor that combines maximum phase features with absolute phase congruency orientation features to effectively address NID. Finally, we present a fine matching method to establish a coarse-to-fine feature matching framework for accurate registration. C2F-AFE not only addresses NID and scale differences but also achieves more reliable matching by extracting more repeatable feature points and enhancing utilization efficiency, thereby improving registration accuracy. Experiments demonstrate that C2F-AFE outperforms existing methods in feature matching and image registration, enabling effective and accurate registration of infrared and visible images.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106199"},"PeriodicalIF":3.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared image nonuniformity correction via dual-stream attention and hybrid domain convolution","authors":"Qintong Li,&nbsp;Yong Ma,&nbsp;Jun Huang,&nbsp;Kangle Wu,&nbsp;Ge Wang","doi":"10.1016/j.infrared.2025.106197","DOIUrl":"10.1016/j.infrared.2025.106197","url":null,"abstract":"<div><div>Infrared imaging is widely used in various fields but is often degraded by nonuniformity noise, which poses significant challenges to image quality. Existing nonuniformity correction (NUC) methods often lack accurate modeling of real-world infrared characteristics and struggle to adapt to complex environments. Moreover, many deep learning-based methods originate from visible image processing and are ineffective in addressing the stripe nonuniformity while also exhibiting limited capacity for global feature extraction. To address these issues, we propose a novel infrared image NUC method that integrates dual-stream attention with hybrid domain convolution. A cross-aware attention module is introduced to enhance sensitivity to nonuniformity features such as stripe noise. Combined with a multi-head self-attention mechanism, it forms a dual-stream attention structure that improves global and structural feature modeling. Additionally, we design a hybrid domain convolution module that jointly leverages spatial and frequency information, enabling effective extraction of both local details and global patterns. We also present a realistic simulation method for generating nonuniformity noise in infrared images, facilitating the construction of a high-quality paired dataset for model training and evaluation. Experimental results demonstrate that the proposed method outperforms advanced methods in both visual quality and quantitative metrics, effectively suppressing various types of nonuniformity noise.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106197"},"PeriodicalIF":3.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145359218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on human action recognition Algorithm in infrared video based on five-dimensional bidirectional dynamic convolution and multi-head attention","authors":"Jiajie Wang,&nbsp;Lei Yu,&nbsp;Shuai Yuan,&nbsp;Jiali Long,&nbsp;Wen Xie,&nbsp;Weiqiang Chen,&nbsp;Bangshu Xiong,&nbsp;Qiaofeng Ou","doi":"10.1016/j.infrared.2025.106196","DOIUrl":"10.1016/j.infrared.2025.106196","url":null,"abstract":"<div><div>Human action recognition is a hot research topic in computer vision. In recent years, infrared imaging technology has shown unique advantages in intelligent security and health monitoring due to its night-time environmental adaptability and privacy protection features. However, existing methods face challenges such as insufficient spatiotemporal feature representation and difficulty in distinguishing highly similar actions in complex scenes due to the inherent characteristics of infrared video, including low resolution and lack of texture information. To address these issues, This paper proposes an improved model based on Five-dimensional Bidirectional Dynamic Convolution and Multi-head Attention mechanism. Firstly, to tackle the feature extraction challenge in infrared video, a five-dimensional bidirectional dynamic convolution module is designed. This module dynamically adjusts convolution kernel parameters through five types of attention weights—spatial, temporal, channel, filter, and kernel dimensions—to enhance sensitivity to low-contrast motion features. Meanwhile, deconvolutional residual connections are introduced to preserve significant spatiotemporal regions and mitigate detail loss. Secondly, to resolve the misclassification problem of highly similar actions, an efficient multi-head separable attention module is proposed. This module reduces computational overhead by sharing query and key parameters for spatial–temporal and channel attention, employs dimensionality reduction projection strategies to compress key-value matrix dimensions, and integrates depthwise separable modules to further optimize feature interaction efficiency. The comparative experiment results show that the proposed method achieved recognition accuracies of 79.37% and 87.56% on the IITR and InfAR datasets, respectively, demonstrating its superiority. The ablation experiment results indicate that our method significantly improves model accuracy and has research value. Code is available at: <span><span>https://github.com/ysls160915/C3D_FBDConv_EMHSA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106196"},"PeriodicalIF":3.4,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145359227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HMANet: Hierarchical Multi-Attention Enhancement Network for infrared small target detection","authors":"Xuyang Li ,&nbsp;Lan Guo ,&nbsp;Yu Shao,&nbsp;Jin-Qiang Wang,&nbsp;Jie Xiao,&nbsp;Binbin Yong,&nbsp;Chuanyi Liu,&nbsp;Qingguo Zhou","doi":"10.1016/j.infrared.2025.106180","DOIUrl":"10.1016/j.infrared.2025.106180","url":null,"abstract":"<div><div>The application of infrared small target detection technology is becoming increasingly widespread in both military and civilian fields, with its strategic value and practical significance being continuously emphasized. Existing CNN-based methods are highly prone to the loss of critical target features in pooling layers and exhibit limited capability in capturing local features of small targets. This leads to significant degradation of spatial information, making it challenging to effectively differentiate small targets from background clutter. As a result, such methods are not directly suitable for infrared small target detection. To address the aforementioned issues, we propose a Hierarchical Multi-Attention Enhancement Network (HMANet) tailored for the precise localization of small infrared targets. Firstly, we design a fusion strategy that integrates spatial-channel attention with frequency-domain attention. This approach builds inter-layer feature correlations using channel attention in the spatial domain. It enhances fine-grained details through multi-scale spectral processing in the frequency domain. Cross-domain feature interaction further improves target-to-background contrast in a hierarchical manner. Secondly, we capture contextual dependencies across feature layers by modeling the relationships between embedded tokens at multiple levels using diverse attention interaction modules. This enables the construction of hierarchical contextual representations, facilitating the accurate detection of infrared small targets. In addition, comprehensive evaluations on the IRSTD-1K and SIRST3 dataset demonstrate that the proposed method achieves competitive performance across all evaluation metrics.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106180"},"PeriodicalIF":3.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QuatFuse: Quaternion-based orthogonal representation learning for multi-modal image fusion","authors":"Weida Wang,&nbsp;Zhuowei Wang,&nbsp;Xingming Liao,&nbsp;Xuanxuan Ma,&nbsp;Siyue Xie,&nbsp;Genping Zhao,&nbsp;Lianglun Cheng","doi":"10.1016/j.infrared.2025.106202","DOIUrl":"10.1016/j.infrared.2025.106202","url":null,"abstract":"<div><div>Multi-modality image fusion (MMIF) is a technique that integrates complementary information from different imaging modalities into a single image, aiming to generate a more comprehensive and information-rich integrated representation. The existing methods focus on using more complex network structures to improve the fusion performance of the model but ignore the correlation between different modal images. To solve this problem, we propose QuatFuse, a Quaternion-Based Orthogonal Representation Learning fusion method. This approach utilizes the mathematical properties of quaternions to model inter-modal relationships. Specifically, we introduce orthogonal geometric constraints and discrete cosine transformations to process redundant information and enhance features across various frequencies, effectively improving QuatFuse’s retention of key features. Fusing high-frequency and low-frequency information from multi-modal images after feature extraction is implemented in the quaternion domain, effectively mapping this processing procedure from the traditional real domain to a higher-dimensional representation space. To validate the robustness of QuatFuse, experiments on Infrared-Visible image fusion (IVF) and Medical image fusion (MIF) are conducted across 6 datasets (comprising 5 public datasets and 1 private dataset), with its performance being measured by eight distinct metrics. Our model achieved state-of-the-art (SOTA) performance on most evaluation metrics, demonstrating its superior fusion capabilities.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106202"},"PeriodicalIF":3.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on contamination-aware adaptive calibration method for wide-band hyperspectral imaging system","authors":"Zhiyuan Ma ,&nbsp;Hongmei Li ,&nbsp;Yujie Xing ,&nbsp;Xuquan Wang ,&nbsp;Xiong Dun ,&nbsp;Zhanshan Wang ,&nbsp;Xinbin Cheng","doi":"10.1016/j.infrared.2025.106192","DOIUrl":"10.1016/j.infrared.2025.106192","url":null,"abstract":"<div><div>Hyperspectral imaging technology has become an important tool in modern optical detection due to its advantage of integrating spectra. When using hyperspectral data for quantitative analysis, radiometric calibration is essential for converting raw digital signals into reflectance. However, standard diffuse panels are inevitably contaminated in practical applications, leading to a decrease in radiometric calibration accuracy and introducing systematic errors. Traditional methods such as manual cleaning are not only expensive to maintain, but also difficult to implement rapidly in unattended automated hyperspectral systems. In this study, we propose a contamination-aware empirical line method (CA-ELM) based on a wide-band hyperspectral imaging system (400–1700 nm), which aims to reduce the effect of localized contamination on the standard diffuse panels in radiometric calibration. By combining spectral feature clustering and spatial edge detection methods, CA-ELM adaptively identifies and excludes contaminated areas of the diffuse panels. Only the field-measured reflectance of the clean areas is reserved for radiometric calibration. In the case of localized contamination of the diffuse panels, the average reflectance error of CA-ELM compared to the empirical line method decreased from 4.58 % to 3.08 %, which approached the performance of calibration based on clean diffuse panels. Further validation using the random forest algorithm for hyperspectral classification of seven samples showed that the model achieved an average classification accuracy of 98.86 % for CA-ELM calibrated images, which was 4.60 % higher than the empirical line method. In the experimental scenario where it is difficult to clean or replace diffuse panels in time, CA-ELM provides an effective solution to the problem that the calibration accuracy decreases due to localized contamination of the panels. This study verifies the feasibility of CA-ELM under laboratory conditions, and provides technical support for the realization of automated and robust hyperspectral radiometric calibration.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106192"},"PeriodicalIF":3.4,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PTDNet: Parallel text-guided distillation network for low-light image fusion","authors":"Quan Wang ,&nbsp;Fengyuan Liu ,&nbsp;Yi Cao ,&nbsp;Farhan Ullah ,&nbsp;Jin Jiang","doi":"10.1016/j.infrared.2025.106198","DOIUrl":"10.1016/j.infrared.2025.106198","url":null,"abstract":"<div><div>A common issue in low-light image fusion is overexposure during the daytime and overexposure of point light sources at night. Existing brightness enhancement networks used in low-light image fusion often lost significant information, due to the lack of supervision from high-brightness images. As a result, it is difficult for the existing low-light image fusion networks to handle complex, low-light environments. To address the challenge of enhancing image details while avoiding information loss, a novel image enhancement and fusion network (PTDNet) is proposed in this paper. PTDNet combines text guidance and knowledge distillation techniques to enhance image details and preserve information in low-light conditions. Moreover, PTDNet employs parallel CNNs and Mamba-based feature extraction and fusion modules, in order to effectively handle overexposure issues in low-light environments while preserving the accuracy and naturalness of image details under various lighting conditions. Therefore, PTDNet cannot only overcomes the brightness inconsistency issues found in traditional methods, but also enhances image visibility and clarity in low-light conditions. In the experimental section, PTDNet was qualitative and quantitatively validated on three datasets. The qualitative experimental results show that PTDNet effectively addresses the overexposure problem in traditional methods, significantly improving image quality and making details more transparent and naturally visualized. The quantitative experimental results indicate that PTDNet performed better on key metrics such as AG, EN, SF, and SD for the LLVIP and MSRS datasets.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106198"},"PeriodicalIF":3.4,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A stable identification method of wool and cashmere based on localized modeling strategy using NIR spectroscopy","authors":"Yaolin Zhu ,&nbsp;Mengyue Hao ,&nbsp;Xingze Wang ,&nbsp;Long Chen ,&nbsp;Xin Chen ,&nbsp;Jinni Chen","doi":"10.1016/j.infrared.2025.106204","DOIUrl":"10.1016/j.infrared.2025.106204","url":null,"abstract":"<div><div>For a long time, accurate identification of cashmere and wool fibers has been a challenge in the textile industry. Traditional chemical and image recognition methods are very complex, time-consuming, and costly. Currently, near-infrared spectroscopy is a new identification method with fast, accurate and non-destructive characteristics. However, there is a dilemma that fibers from different pastures exhibit intra-class differences is always bigger than inter-class differences in spectra, which leads to increased difficulty in identification due to spectral overlap. Therefore, this paper proposes a localized modeling strategy based on the clustering method to balance intra-class and inter-class differences and reduce spectral overlap. The strategy uses the Kennard-Stone (KS) algorithm to select representative samples with a concentrated feature distribution to determine the distribution range of each class, and then calculates the distance between each sample and the representative samples by using the Spectral Angle Mapper (SAM), which divides samples with similar characteristics into the same clusters according to a set distance threshold. This method reduces the spectral overlap rate by re-filtering and dividing the feature distribution of each class of samples into clusters. Experimental results demonstrate that the proposed localized modeling strategy can achieve prediction accuracy of up to 97.8 %, surpassing the 90.7 % accuracy obtained by training a global model with all samples. Therefore, the proposed strategy in this study can effectively reduces the impact of spectral overlap, and improves the recognition accuracy of cashmere and wool.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106204"},"PeriodicalIF":3.4,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A distributed sensor-based method for tracking and localization of space target groups","authors":"Yao Li ,&nbsp;Yueqi Su ,&nbsp;Xin Chen ,&nbsp;Peng Rao","doi":"10.1016/j.infrared.2025.106188","DOIUrl":"10.1016/j.infrared.2025.106188","url":null,"abstract":"<div><div>Low-orbit infrared sensors are an important means of space exploration and hold significant importance for space security. However, the detection range of individual satellites is limited, presenting challenges in fulfilling the task of continuous indication of targets. Joint exploration using multiple sensors is a more effective choice. Therefore, we propose a multi-target tracking and localization algorithm based on cooperative detection using multiple infrared sensors. This algorithm enables an integrated process from image plane tracking to three-dimensional spatial localization of small target groups. Firstly, we propose a multi-target tracking method using an improved discriminative correlation filter as the tracker. The method sets an energy concentration threshold based on the characteristics of infrared small targets to suppress background noise. Simultaneously, the minimum Euclidean distance and velocity similarity between consecutive frames of targets are used to associate the trajectories, effectively reducing association errors. In addition, an adaptive extended Kalman filter algorithm is synchronized to predict the target positions, addressing the challenge of small targets being easily occluded. Subsequently, an adaptive weighted covariance intersection fusion algorithm is employed to integrate multi-sensor information of tracking, effectively mitigating the issue of reduced localization accuracy caused by instability or tracking errors in individual sensors. Experimental results show that the mean Optimal SubPattern Assignment of the proposed tracking method is less than 0.2 pixels in simulated multi-target scenarios. The proposed multi-sensor fusion algorithm ensures localization accuracy within 44 m for detection ranges exceeding 4000 km. This highlights its potential in the fields of space exploration and target indication.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106188"},"PeriodicalIF":3.4,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The irradiation damage mechanism and performance enhancement of thermochromic VO2 films under high-power laser irradiation","authors":"Xueyu Wu ,&nbsp;YuQi Wang ,&nbsp;Le Yuan ,&nbsp;Lun Qi ,&nbsp;Xiaolong Weng ,&nbsp;Mei Bi ,&nbsp;Ke Ren","doi":"10.1016/j.infrared.2025.106195","DOIUrl":"10.1016/j.infrared.2025.106195","url":null,"abstract":"<div><div>Vanadium dioxide (VO₂) is a phase-change material with tunable optical properties, demonstrates significant potential for smart laser protection applications. However, its practical implementation has been constrained by relatively low damage thresholds and insufficient laser irradiation resistance. This study employed atomic layer deposition (ALD) to fabricate both VO₂ and Al₂O₃/VO₂ thin films, subsequently investigating their damage mechanisms and protective performance under 1060 nm laser irradiation. Key findings reveal that ALD-prepared VO₂ films exhibit exceptional optoelectronic response characteristics, achieving 75 % infrared transmittance modulation in the 3–5 μm wavelength range accompanied by five orders of magnitude resistance variation. Nevertheless, laser-induced oxidation was identified as the primary degradation mechanism, causing up to 89.7 % deterioration in protective performance. The integration of a 10 nm Al₂O₃ protective layer not only preserved the desirable optical response properties but also reduced performance degradation to merely 6.9 % under identical irradiation conditions while extending service life by over fourfold. These results present a novel approach for developing high-threshold laser protective film.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"152 ","pages":"Article 106195"},"PeriodicalIF":3.4,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}