EVALUATION OF SMALL ANIMAL IMAGING UNDER DIFFERENT SCAN CONDITIONS

To evaluae small animal imaging with individual different high voltage, filter thickness and tube current, an animal X-ray micro-computed tomography （micro-CT） system based on panel detector is developed and a rat is scanned by using the system with individual high voltage, tube current, filter thickness, and exposure time. A model is presented based on the Monte Carlo code PENELOPE for generating the X-ray spectra of X-ray tube used in the micro-CT system. A platform developed based on Matlab allows for calculating beam quality parameters, including the average energy of X-ray beam, the change of transmition rate and the input X-ray fluence. The factors affecting the signal difference to noise ratio （SDNR） of micro-CT are investigated and the relationship between SDNR and scan combinations is analyzed. A series of tools and methods are developed for small animal imaging and imaging performance evaluation in the field of small animal imaging.

Objective measurement for image defogging algorithms

Since there is lack of methodology to assess the performance of defogging algorithm and the existing assessment methods have some limitations,three new methods for assessing the defogging algorithm were proposed.One was using synthetic foggy image simulated by image degradation model to assess the defogging algorithm in full-reference way.In this method,the absolute difference was computed between the synthetic image with and without fog.The other two were computing the fog density of gray level image or constructing assessment system of color image from human visual perception to assess the defogging algorithm in no-reference way.For these methods,an assessment function was defined to evaluate algorithm performance from the function value.Using the defogging algorithm comparison,the experimental results demonstrate the effectiveness and reliability of the proposed methods.

A method to generate foggy optical images based on unsupervised depth estimation

For traffic object detection in foggy environment based on convolutional neural network(CNN),data sets in fog-free environment are generally used to train the network directly.As a result,the network cannot learn the object characteristics in the foggy environment in the training set,and the detection effect is not good.To improve the traffic object detection in foggy environment,we propose a method of generating foggy images on fog-free images from the perspective of data set construction.First,taking the KITTI objection detection data set as an original fog-free image,we generate the depth image of the original image by using improved Monodepth unsupervised depth estimation method.Then,a geometric prior depth template is constructed to fuse the image entropy taken as weight with the depth image.After that,a foggy image is acquired from the depth image based on the atmospheric scattering model.Finally,we take two typical object-detection frameworks,that is,the two-stage object-detection Fster region-based convolutional neural network(Faster-RCNN)and the one-stage object-detection network YOLOv4,to train the original data set,the foggy data set and the mixed data set,respectively.According to the test results on RESIDE-RTTS data set in the outdoor natural foggy environment,the model under the training on the mixed data set shows the best effect.The mean average precision(mAP)values are increased by 5.6%and by 5.0%under the YOLOv4 model and the Faster-RCNN network,respectively.It is proved that the proposed method can effectively improve object identification ability foggy environment.

Simulation Study on SAR Images of the Oceanic Internal Waves

Based on the research of Lynett and Liu, the horizontal fully two-dimensional, depth-integrated model for the internal wave propagation is re-deduced. By combining this model with the M4S model, the propagation process of the internal waves is simulated in Synthetic Aperture Radar （SAR） images. The simulation results clearly show the bottom effects during the propagation such as fission and isobaths-parallelized propagation direction. This simulation procedure can lay the foundation for the quantitative interpretation of internal waves from fully two-dimensional SAR images.

A Wave Propagation Model Based on Monte-Carlo Particle-Tracing

This article describes a new wave propagation model based on Monte-Carlo particle-tracing. This model relies on Monte-Carlo integration and Huygens currents radiating. The particles used to compute the field permit to consider the interferences. This model includes the diffraction of the surface without edge computation. The implementation of this propagation model is based on a image synthesis renderer. The results of this model are studied in far field situation with perfectly conducting shapes, by comparing results with a classical MoM method.

再生粗集料二维形态结构分析

针对回收沥青混合料,采用图像成型方法,以椭圆度、形态因子参数为指标,对比分析了回收集料与新集料的二维形态结构及形态特征,研究了不同新旧比例再生集料的二维形态结构变化规律。结果表明:随着集料粒径的增大,椭圆度逐渐下降,回收集料较新集料的椭圆度变化较小;再生集料的形态因子随粒径增大逐渐降低;通过对再生集料形态结构分析,回收集料的掺量对再生集料的形态因子和椭圆度值影响较小,说明再生集料形态结构变化不明显。

A Novel Method for Multi-image Matching Synthesizing Image and Object-space Information

A novel method for multi-image matching by synthesizing image and object-space information is proposed. Firstly, four levels of image pyramids are generated according to the rule that the next pyramid level is generated from the previous level using the average gray values of the 3 by 3 pixels, and the first level of pyramid image is generated from the original image. The initial horizontal parallaxes between the reference image and each searching image are calculated at the highest level of the image pyramid. Secondly, corresponding image points are searched in each stereo image pair from the third level of image pyramid, and the matching results in all stereo pairs are integrated in the object space, by which the mismatched image points can be eliminated and more accurate spatial information can be obtained for the subsequent pyramid image matching. The matching method based on correlation coefficient with geometric constraints and global relaxation matching is introduced in the process of image matching. Finally, the feasibility of the method proposed in this paper is verified by the experiments using a set of digital frame aerial images with big overlap. Compared with the traditional image matching method with two images, the accuracy of the digital surface model (DSM) generated using the proposed method shows that the multiimage matching method can eliminate the mismatched points effectively and can improve the matching success rate significantly.

Modeling of information images dynamics through the communicative field method

This work presents a formalized description of information and communicative interac- tions of individuals on the basis of the communicative field （CF） method. It also contains explication of the possibility to create model of information and communication inter- actions, which is able to illustrate both interactions between two and more individuals. Methods and approaches which are suggested in this paper can correctly simulate the processes of distortion and generation of information images （IIs） with information and communication social interaction. Expansion and addition of IIs theory in terms of the transmission of information between individuals allows us to speak about the space of IIs. This space helps to explain a number of characteristic phenomena of human thinking.