An approach for haze removal utilizing polarimetric imaging and multi-scale analysis has been developed to solve one problem that haze weather weakens the interpretation of remote sensing because of the poor visibility and short detection distance of haze images. On the one hand, the polarization effects of the airlight and the object radiance in the imaging procedure has been considered. On the other hand, one fact that objects and haze possess different frequency distribution properties has been emphasized. So multi-scale analysis through wavelet transform has been employed to make it possible for low frequency components that haze presents and high frequency coefficients that image details or edges occupy are processed separately. According to the measure of the polarization feather by Stokes parameters, three linear polarized images (0°, 45°, and 90°) have been taken on haze weather, then the best polarized image min I and the worst one max I can be synthesized. Afterwards, those two polarized images contaminated by haze have been decomposed into different spatial layers with wavelet analysis, and the low frequency images have been processed via a polarization dehazing algorithm while high frequency components manipulated with a nonlinear transform. Then the ultimate haze-free image can be reconstructed by inverse wavelet reconstruction. Experimental results verify that the dehazing method proposed in this study can strongly promote image visibility and increase detection distance through haze for imaging warning and remote sensing systems.
A simulation method for analyzing polarization states for infrared scenes is proposed in order to study the polarization features of infrared spontaneous emission deeply, since current infrared polarization devices can’t show the polarization signature of infrared spontaneous emission for a target or an object well. A preliminary analysis on polarization characteristics of infrared spontaneous emission in the ideal case is carried out and also a corresponding ideal model is established through Kirchhoff’s law and the Fresnel theorem. Based on the newly built ideal model, a three-dimensional (3D) scene modeling and simulation based on the OpenSceneGraph (OSG) rendering engine is utilized to obtain the polarization scene of infrared emission under ideal conditions. Through the corresponding software, different infrared scenes can be generated by adjusting the input parameters. By interacting with the scene, the infrared polarization images can be acquired readily, also a fact can be obviously confirmed that the degree of linear polarization (DoLP) for an object in the 3D scene varies with the many factors such as emission angle and complex refractive index. Moreover, large difference between two kinds of material such as metal and nonmetal in the polarization characteristics of infrared spontaneous emission at the same temperature can be easily discerned in the 3D scene. The 3D scene simulation and modeling in the ideal case provides a direct understanding on infrared polarization property, which is of great significance for the further study of infrared polarization characteristics in the situation of real scenes.
Aiming to realize super resolution(SR) to single image and video reconstruction, a super resolution camera model is proposed for the problem that the resolution of the images obtained by traditional cameras behave comparatively low. To achieve this function we put a certain driving device such as piezoelectric ceramics in the camera. By controlling the driving device, a set of continuous low resolution(LR) images can be obtained and stored instantaneity, which reflect the randomness of the displacements and the real-time performance of the storage very well. The low resolution image sequences have different redundant information and some particular priori information, thus it is possible to restore super resolution image factually and effectively. The sample method is used to derive the reconstruction principle of super resolution, which analyzes the possible improvement degree of the resolution in theory. The super resolution algorithm based on learning is used to reconstruct single image and the variational Bayesian algorithm is simulated to reconstruct the low resolution images with random displacements, which models the unknown high resolution image, motion parameters and unknown model parameters in one hierarchical Bayesian framework. Utilizing sub-pixel registration method, a super resolution image of the scene can be reconstructed. The results of 16 images reconstruction show that this camera model can increase the image resolution to 2 times, obtaining images with higher resolution in currently available hardware levels.
A concise band selection method employing multispectral signatures of stealth aircraft whose infrared radiation was remarkably reduced was proposed for precise target detection. The key step was to select two or more optimal bands which could clearly signify the radiation difference between the target and its background. The principle of preliminary selection was based on the differences of radiation characteristics for the two main constituents of the aircraft’s plume gas, i.e., CO2 and H2O. Two narrow bands of 2.86 to 3.3 and 4.17 to 4.55 μm were finally selected after detailed analyses on contrast characteristics between the target and background. Also, the stability of the selected bands was tested under varying environments. Further simulations and calculations demonstrated that the multispectral detection method utilizing the two selected narrow bands could markedly improve the essential performances of target detection systems and increase their achievable detection distance. The stability of the aircraft’s multispectral signatures enabled this target detection method to achieve excellent results.
To acquire high-resolution IR polarization images, a pixel-level image reconstruction method was introduced. It was aimed at IR polarization imaging systems employing multi-aperture principle. The geometric mapping relation between images was firstly studied and was basis of this method. Parameters of the mapping relation were calculated, and then pixels of each image obtained were mapped to a virtual digital plane at which precise and resolution enhanced polarization images could be obtained by taking advantage of the pixel deviation and rearranging the pixels. Experimental results demonstrated that the algorithm could assist the multi-aperture imaging system in rendering easily precise and high-resolution polarization images.
A novel IR polarization staring imaging system employing a four-camera-array is designed for target detection and recognition, especially man-made targets hidden in complex battle field. The design bases on the existence of the difference in infrared radiation’s polarization characteristics, which is particularly remarkable between artificial objects and the natural environment. The system designed employs four cameras simultaneously to capture the00 polarization difference to replace the commonly used systems engaging only one camera. Since both types of systems have to obtain intensity images in four different directions (I0 , I45 , I90 , I-45 ), the four-camera design allows better real-time capability and lower error without the mechanical rotating parts which is essential to one-camera systems. Information extraction and detailed analysis demonstrate that the caught polarization images include valuable polarization information which can effectively increase the images’ contrast and make it easier to segment the target even the hidden target from various scenes.
KEYWORDS: Signal to noise ratio, Signal processing, Signal detection, Laser systems engineering, Laser countermeasures, Interference (communication), Detection theory, Electronic filtering, Optical filters, Laser applications
It is very important to extract the useful information from weak laser signal which is obtained in complex battlefield environment as laser warning taking an increasingly important role in laser countermeasures. The weak signal merging in noise becomes difficult to detect since the signal to noise ratio (SNR) of the signal received by the laser warning system is very low in real battlefield. Traditional signal detection methods, in which only mean filter or wiener filter are used; perform poorly in improving the SNR of the signals. A modified matrix of Hadamard Transform based on the Weighting Theory, overcame the disadvantages of matrices that are commonly used to cope with the low SNR signal. The modified matrix generating method of Hadamard Transform is introduced in detail, and then theory analysis, calculations and simulations on the modified matrix Hadamard Transform are presented. The results showed that this kind of Hadamard Transform performs excellently in increasing detection probability and decreasing False Alarm Ratio (FAR) of the laser warning system.
An improved optical structure of a laser warning system based on microlens array is proposed aiming at the high precision imaging warning system with a smaller size. Microlens array owning the advantage of high efficiency of energy use, motion sensitivity, etc. as a multi-aperture optical element, is applied in many optical systems. It is a tough task to obtain satisfied images with a curved base microlens array because the widely used plane detectors are not fit for these kinds of microlens array with poor imaging quality though it achieves the goal of wide field of view (FOV). We address to design a model by combining the curved base microlens array with the aspheric converging lens to solve the poor imaging quality caused by cured base microlens array. This method will make it possible to enlarge the FOV with better image quality. The ray tracing results show that the image quality acquired from plane detectors is improved using curved base microlens array, but with more simple fabricated structure than that of fisheye lens, which is widely used to get a wider FOV.
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