This study suggests a multi-scale polarization fusion method based on lifting wavelet transform to enhance the clarity and contrast of underwater polarization images, which are currently low contrast, of poor quality, and blurred texture features. In the experiment, underwater polarization imaging of several materials using a 532 nm blue-green laser was used, and intensity photographs taken from various angles were collected. The polarization degree and polarization Angle of the image are calculated, and the polarization combination image is generated according to the method of region energy maximization. The polarization combination image and light intensity image are decomposed into low frequency components reflecting contour information and high frequency components reflecting detail information at multiple scales. The regional energy as the weight coefficient and the fusion rules based on the combination of region variance are constructed and applied to the low frequency and high frequency components respectively. Experimental simulations demonstrate that this fusion method has some advantages over conventional image fusion techniques in terms of boosting image contrast and clarity. It has also established a strong base for resource discovery, bottom topography exploration, submarine detection, and other sectors.
The traditional fusion method for polarization image and color image is difficult to be satisfied in practice, so the method based on SCM in the fusion of polarization image and color image is designed and combined with HSV color field adaptive enhancement, so as to achieve the effect of distinguishing objects under shadow or reflection. Using the multi-scale decomposition of guided filter and Gaussian filter, different fusion schemes are proposed for different scale information: the average energy fusion method is used for the low-frequency coefficients, and the SCM with the edge energy (EOE) as the stimulation is used to replace the traditional high-frequency fusion rules, so as to enhance the spatial information expression. Finally, the parameters are corrected by the consistency test. Experiments show that this method not only has a good color expression ability, but also effectively adds the polarization information of the target object, and achieves good performance in objective and supervisor evaluation.
The simulation of 3D clouds has been a challenging research question in the field of computer graphics. Aiming at the problem that the existing three-dimensional cloud is not realistic, a three-dimensional particle cloud simulation method based on the illumination model is proposed, which randomly generate the particles according to the principle of the particle system and give the particles the initial color, size and shape. And then add the lighting effects and render them to achieve the three-dimensional cloud simulation. Comparing with the previous three-dimensional cloud modeling method, this method has the advantages of rapid rendering of cloud, because of the effect of adding light, the real feeling more intense.
In the domain of target recognition, the image complexity of target and background is used to describe the difficult degree of extracting and recognizing target from complex background, which has important guiding significance and widely application prospect in a lot of domains such as biological medical, information encrypt, image compression, meteorological analysis, automatic target recognition. This paper comprehensively took the innate characteristics of target and the target local background characteristic into consideration, which affected the algorithm performance of target extraction and recognition, then made generalizations of three classes of evaluation methods: methods based on the target characteristic, including the target shape characteristic, the gray standard deviation of target pixels, the target Local background entropy difference, etc; methods based on the target similitude, including the edge profile and structural characteristic similitude between target and phony target; methods based on the background characteristic, including texture characteristic edge ratio, etc. And on this basis, we made research on the relationship of structural features and evaluation parameters, and analyzed the foundation and properties of each method by contrast. Thoughts and foresights of this field are given at the end of this paper.
Target recognition is widely used in national economy, space technology and national defense and other fields. There is great difference between the difficulty of the target recognition and target extraction. The image complexity is evaluating the difficulty level of extracting the target from background. It can be used as a prior evaluation index of the target recognition algorithm's effectiveness. The paper, from the perspective of the target and background characteristics measurement, describe image complexity metrics parameters using quantitative, accurate mathematical relationship. For the collinear problems between each measurement parameters, image complexity metrics parameters are clustered with gray correlation method. It can realize the metrics parameters of extraction and selection, improve the reliability and validity of image complexity description and representation, and optimize the image the complexity assessment calculation model. Experiment results demonstrate that when gray system theory is applied to the image complexity analysis, target characteristics image complexity can be measured more accurately and effectively.
With the development of electro-optical imaging system technology and simulation technology,
and the demand of optimizing the new type electro-optical imaging system theoretical model, more and
more scientific research institutes, colleges and universities research on the simulation of
electro-optical imaging system, and the better results were obtained. Simulation technology saved the
cost of system design development, meanwhile, some complex and hard to re-implement experiments
can be carried repeatedly. According to the demand of complex environment construction technology
and the requirement of imaging simulation system fidelity, considering the performance of
electro-optical imaging system, an electro-optical imaging system is modeled. The modeling has two
aspects which is scene characteristic modeling and electro-optical system modeling. Scene
characteristic modeling can construct dynamic scenes in different kinds of complex environments by
using powerful OpenGL three-dimension model visualization technology. Electro-optical system
modeling is consist of optical system and imaging detector. Electro-optical imaging system simulation
model is established with the analysis of electro-optical imaging system theory. The use of modular
design concept and general interface technology is combined. Different imaging effect is received
under different parameters by modifying the model’s related parameters. The experimental results show
that, the image produced from simulation basically reflects the performance of imaging system, so this
kind of image can be used as a information source for imaging system performance analysis. It
provides a simple and feasible method for the analysis of imaging system performance, which has a
very important practical significance.
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