Generation of Dynamic Infrared Outer Space Scene

This paper proposes a new method to generate the dynamic infrared outer space scene in real time. In the paper, we first model the dynamic infrared background of star sky based on the infrared star catalogue and temporal and spacial coordinate transformations. We also present a new concept called infrared star magnitude for given infrared receiving wavebands to display the infrared star sky effectively. Then we put forward an infrared image synthesis model for outer space targets based on the method of divided elements and energy equilibrium. To generate the dynamic infrared outer space scene, several simplification and accelerating techniques such as Billboard, Levels of Details and Occlusion Culling are hired to accelerate the generating speed of the scene. Finally infrared images of targets, background of outer space scene at different infrared receiving wavebands are drawn and the dynamic infrared outer space scene is successfully carried out in real time. * Corresponding author     

Dynamic infrared scene projection: a review

Since the early 1990s, there has been major progress in the developing field of dynamic infrared scene projection, driven principally by the need for hardware-in-the-loop simulation of the oncoming generation of imaging infrared missile seekers and more recently by the needs for realistic simulation of the new generation of thermal imagers and forward-looking infrared systems. In this paper the current status of the dynamic infrared projection field is reviewed, commencing with an outline of its history. The requirements for dynamic infrared scene projection are examined, allowing a set of validity criteria to be developed. Each class of infrared projector that has been investigated—emissive, transmissive, reflective, laser scanner and phosphor—together with the specific technology initiatives within the class is described and examined against the validity criteria. In this way the leading dynamic infrared scene projection technologies are identified.     

Generation of Realistic Infrared Image for Moving Objects

Most of current methods are available for synthesizing the static infrared scene, but fail to simulate the infrared signature of the objects caused by motion. This paper presents a new method for generating realistic infrared image of moving objects. Considering various environmental factors, object geometry and structure, internal heat conditions and state of movement, an infrared image synthesis model for moving objects is established. Based on this model the rendering method for infrared image of this kind of objects is proposed. By incorporating the effect of atmosphere transmission and the noise mechanism of infrared sensor, the infrared images of the working aircraft and automobile are successfully generated from different viewpoint. Experimental results illustrate the potential of our method.     

High dynamic range compression and detail enhancement of infrared images in the gradient domain

To find the trade-off between providing an accurate perception of the global scene and improving the visibility of details without excessively distorting radiometric infrared information, a novel gradient-domain-based visualization method for high dynamic range infrared images is proposed in this study. The proposed method adopts an energy function which includes a data constraint term and a gradient constraint term. In the data constraint term, the classical histogram projection method is used to perform the initial dynamic range compression to obtain the desired pixel values and preserve the global contrast. In the gradient constraint term, the moment matching method is adopted to obtain the normalized image; then a gradient gain factor function is designed to adjust the magnitudes of the normalized image gradients and obtain the desired gradient field. Lastly, the low dynamic range image is solved from the proposed energy function. The final image is obtained by linearly mapping the low dynamic range image to the 8-bit display range. The effectiveness and robustness of the proposed method are analyzed using the infrared images obtained from different operating conditions. Compared with other well-established methods, our method shows a significant performance in terms of dynamic range compression, while enhancing the details and avoiding the common artifacts, such as halo, gradient reversal, hazy or saturation.     

An Infrared Image Synthesis Model for High-Speed Targets

Although a number of infrared models are available for synthesizing the infrared image of static objects, they fail to simulate the infrared signature of targets flying at high speed. This paper presents an infrared image synthesis model for such kind of targets. Within this model, we account for not only all forms of heat transfer inside the targets but also the dynamic interaction between the target's surface and the surrounding air-flow. The heat transfer process between the target's surface and the air-flow is carefully analyzed based on the principles of infrared physics, aerodynamics and heat transfer, and a term due to aerodynamic heating is incorporated into our new model. Experimental results illustrate the potential of the model     

一种改进的红外图像归一化互相关匹配算法

分析了传统归一化互相关算法在红外空中目标匹配定位时失效的原因,提出一种改进的红外图像归一化互相关匹配算法.该方法将模板和匹配区域之间的纹理相关计算看作一个最优化问题,寻求使图像纹理相关匹配鲁棒性最好的相关基准值,用图像的相关基准函数替代传统方法中的区域均值部分,构造了一种适用于的红外目标匹配的归一化相关算法.实验结果表明,该相关匹配算法对模板中背景部分的变化和非均匀性亮度变化有良好的抗干扰能力,较好地解决了恶劣环境下红外对空目标跟踪中匹配定位出错的问题.     

A Global Infrared Image Synthesis Model for Large-Scale Complex Urban Scene

Most of infrared scene models deal with backgrounds, they can't simulate infrared urban scene. This paper presents a global infrared image synthesis model for large-scale complex urban scene which can be applied to various weather conditions and suitable for various parts of the urban scene. We first analysis the influence of all kinds of meteorological and environmental factors on the infrared characteristic of urban scene. Then based on principle of energy equilibrium we propose a global infrared image synthesis model for urban scene, discuss the specific forms for various parts of the scene and compute their surface temperature. As the rendering principle for infrared scene differs from that for visible scene, we propose a novel rendering criterion for infrared scene. Finally by the method of Gouraud Shading of Computer Graphics, infrared images of urban scene from different viewpoints at different time are presented. Field measurement further shows that our model is robust and feasible.     

Infrared optical modulators for missile testing

The continued proliferation of imaging infrared (I²R) missile systems has created the need for fully representative infrared scene generators. In order to test these I²R systems correctly a very large dynamic range is required of the scene generator. This cannot easily be produced by the standard approach of an array of suspended resistor heater elements. This paper therefore describes the fabrication of infrared optical modulators for use in high dynamic range and high frame rate scene generation. Modulation is produced by the heating of a variable reflectivity coating. This is achieved by coating a suitable substrate with a thin film of vanadium dioxide (VO₂) via a reactive sputtering process. VO₂ undergoes a semiconductor to metal phase transition at approximately 68°C and the associated change in reflectivity is exploited to create the modulator. The correct stoichiometry of the thin film of VO₂ is critical in producing high frame rate devices. Both transmissive and reflective modulators are described.     

Noise suppression and details enhancement for infrared image via novel prior

Infrared images always suffer from blurring edges, fewer details and low signal-to-noise ratio. So, sharpening edges and suppressing noise become the urgent techniques in infrared image technology field. However, they are contradictories in most cases. Hence, to depict correctly infrared image features under low signal-to-noise ratio circumstance, a novel prior, which is immune to noise, is presented in this paper. The proposed method scopes noise suppression and details enhancement. In noise suppression, the prior is introduced into Bayesian model to obtain optimal estimation through iteration. In details enhancement, based on the proposed prior, the final image is obtained by the improved unsharp mask algorithm which enhances adaptively details and edges of optimal estimation. The effectiveness and robustness of the proposed method is analyzed by testing the infrared images obtained from different signal-to-noise ratio conditions. Compared with other well-established methods, the proposed method shows a significant performance in terms of noise suppression, actual scene reappearance, enhancing the details and sharpening edges.     

Analysis of the features and reconstruction of a high resolution infrared image based on a multi-aperture imaging system

Infrared images of good quality are strictly important for such applications as targets detection, tracking and identifying. Traditional single aperture infrared imaging system brings in some defects for its imaging scheme. Multi-aperture imaging system shows promising characteristic of improving image quality and reducing size of optical instruments. We reconstruct a high resolution infrared image from the low resolution sub-images collected by the compact multi-aperture imaging system. A novel reconstruction method called pixels closely arrange (PCA) is proposed based on analyzing the compound eye imaging process, and this method is verified in a simulated 3D infrared scene to capture sub-images. An evaluation of the reconstructed image quality is presented to discuss the significant factors that affect the final result. Experimental results show that the PCA method can be efficiently applied to the multi-aperture infrared imaging system as long as the structure of the micro-lens array is specifically designed to be adaptive to the infrared focal plane array (IFPA).     

Single infrared image super-resolution combining non-local means with kernel regression

In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.     

Infrared moving object detection based on local saliency and sparse representation

The key issue of infrared object detection is to locate moving object in image sequence. In order to improve detection precision, an infrared object detection method based on local saliency and sparse representation is proposed in this paper. Motion information, such as velocity, acceleration components are added into the eigenvectors to build local saliency model. And the approximate position of the infrared target is located based on the local saliency. To accurately extract the infrared object, sparse representation is used to capture complete edge of the object. Experiments show that the proposed method can accurately detect infrared moving objects, and has good robustness to external disturbances and dynamic background.     

Improvement in adaptive nonuniformity correction method with nonlinear model for infrared focal plane arrays

The scene adaptive nonuniformity correction (NUC) technique is commonly used to decrease the fixed pattern noise (FPN) in infrared focal plane arrays (IRFPA). However, the correction precision of existing scene adaptive NUC methods is reduced by the nonlinear response of IRFPA detectors seriously. In this paper, an improved scene adaptive NUC method that employs “S”-curve model to approximate the detector response is presented. The performance of the proposed method is tested with real infrared video sequence, and the experimental results validate that our method can promote the correction precision considerably.     

Salient contour extraction from complex natural scene in night vision image

The theory of center-surround interaction in non-classical receptive field can be applied in night vision information processing. In this work, an optimized compound receptive field modulation method is proposed to extract salient contour from complex natural scene in low-light-level (LLL) and infrared images. The kernel idea is that multi-feature analysis can recognize the inhomogeneity in modulatory coverage more accurately and that center and surround with the grouping structure satisfying Gestalt rule deserves high connection-probability. Computationally, a multi-feature contrast weighted inhibition model is presented to suppress background and lower mutual inhibition among contour elements; a fuzzy connection facilitation model is proposed to achieve the enhancement of contour response, the connection of discontinuous contour and the further elimination of randomly distributed noise and texture; a multi-scale iterative attention method is designed to accomplish dynamic modulation process and extract contours of targets in multi-size. This work provides a series of biologically motivated computational visual models with high-performance for contour detection from cluttered scene in night vision images.     

A categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference features

The fusion of infrared polarization and intensity image can significantly improve the detection performance of target, and the fused image is more suitable for human visual perception and further image-processing tasks. In this paper, a new categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference feature is proposed. Firstly, the difference feature between two kinds of image and the characteristics of different fusion algorithms are analyzed and summarized. Second, an evaluation vector of fusion algorithm for difference feature transform ability is constructed. Thirdly, the transfer ability of fusion algorithm for difference feature is estimated by the evaluation vector, and the degree of transfer ability of fusion algorithm for difference feature is analyzed. Finally the fusion algorithms are classified by the degree of transfer ability of fusion algorithm for difference feature. The results shows that the proposed fusion algorithm categorization method helps select fusion algorithms in actual scene.     

An infrared thermal image processing framework based on superpixel algorithm to detect cracks on metal surface

Infrared thermography has been used increasingly as an effective non-destructive technique to detect cracks on metal surface. Due to many factors, infrared thermal image has low definition compared to visible image. The contrasts between cracks and sound areas in different thermal image frames of a specimen vary greatly with the recorded time. An accurate detection can only be obtained by glancing over the whole thermal video, which is a laborious work. Moreover, experience of the operator has a great important influence on the accuracy of detection result. In this paper, an infrared thermal image processing framework based on superpixel algorithm is proposed to accomplish crack detection automatically. Two popular superpixel algorithms are compared and one of them is selected to generate superpixels in this application. Combined features of superpixels were selected from both the raw gray level image and the high-pass filtered image. Fuzzy c-means clustering is used to cluster superpixels in order to segment infrared thermal image. Experimental results show that the proposed framework can recognize cracks on metal surface through infrared thermal image automatically.     

Infrared Image Series Automatic Registration and a History & Tendency Analysis

Infrared camera has been widely used in electrical power systems for predictive and preventive thermal fault diagnosis. Without historical IR images being considered, most thermograph analysis used at present are concerned with static image. The dynamic history and tendency analysis which compares the newly taken image with historical images is particularly attractive. Conclusions can be drawn from temperature variations rather than just from anomalous thermal pattern. However the spatial distortions prevent such dynamic analysis from being practicable. So the need to register infrared images arises. This paper proposes an automatic registration algorithm based on closed temperature contour matching for infrared image series of a target equipment taken at different times. Centroid Distance Function is used in image registration for the first time. The algorithm can remove spatial distortions, bring temperature variations into prominence and endow infrared camera with dynamic analysis ability. Experiment shows that the algorithm is simple, effective and extensible.     

零视距地物长波红外特征场景仿真研究

为仿真地物长波红外场景图像,根据地表温度随时间变化的规律,并结合气象状况、背景材质、热特性参量、热状态等参数,在对太阳辐射、大气长波辐射、大气温度和地表热传导等影响地表温度变化的因素进行分析的基础上,建立了基于热平衡理论和热传导过程的方程。解算出多种常见地表一日之中的温度变化情况,并将其应用于由相同景物可见光纹理图像反演出的相应红外纹理图像中。在考虑景物表面自身发射、反射的辐射计算模型的前提下,生成了具有相似红外纹理细节的地表红外场景。结果表明,该方法可生成接近真实感的红外场景,有效地模拟仿真地物的长波红外特征。     

适应积分时间调整的红外图像非均匀性校正方法

针对应用常规红外图像非均匀性校正方法在变积分时间时,图像灰度值会发生改变的现象,提出了一种适应积分时间调整的红外图像非均匀性校正方法.该方法将不同积分时间、不同温度的黑体定标数据和对应的理论红外辐射量整合为一个整体数据库,借助神经网络损失函数和误差反向传递机制,对模型中的校正系数进行学习.训练得到的校正网络能在红外相机积分时间实时调整过程中,保证图像均匀地稳定输出,对后端红外图像处理有着重要意义,并验证训练该网络不需要大量定标数据.而针对红外探测器响应漂移的现象,则提出了在线修正校正系数的方法以有效应对.