Xiangzhi Bai

Analysis of new top-hat transformation and the application for infrared dim small target detection

To improve the performance of top-hat transformation for infrared dim small target detection in a simple and effective way, the definition, properties, multi-scale operations of new top-hat transformation and the application for infrared dim small target detection are addressed in this paper. The definition of new top-hat transformation uses two different but correlated structuring elements to reorganize the classical top-hat transformation, and takes into account of the difference information between the target and surrounding regions. Given this definition, the new top-hat transformation has some special properties and three types of multi-scale operations, which are discussed in detail. Subsequently, one application case of multi-scale operation for noise suppression is given. Good performance of the application for infrared dim small target detection is obtained, which could be ascribed to the proper selection of structuring elements based on the properties. The experimental results of the application demonstrate that new top-hat transformation can detect infrared dim small target more efficiently than classical top-hat transformation and some other widely used methods.

Splitting touching cells based on concave points and ellipse fitting

A new touching cells splitting algorithm based on concave points and ellipse fitting is proposed in this paper. The algorithm includes two parts: contour pre-processing and ellipse processing. The purpose of contour pre-processing is to smooth fluctuations of the contour, find concave points of the contour and divide the contour into different segments via the concave points. The purpose of ellipse processing is to process the different segments of the contour into possible single cells by using the properties of the fitted ellipses. Because concave points divide the whole contour of touching cells into different segments and different segments of one single cell have similar properties, the ellipse processing can separate the touching cells through ellipse fitting. This paper demonstrates a new way of using ellipse fitting to split the binary contour of touching cells. Experimental results show that our algorithm is efficient.

Fusion of infrared and visual images through region extraction by using multi scale center-surround top-hat transform

Fusion of infrared and visual images is an important research area in image analysis. The purpose of infrared and visual image fusion is to combine the image information of the original images into the final fusion result. So, it is crucial to effectively extract the image information of the original images and reasonably combine them into the final fusion image. To achieve this purpose, an algorithm by using multi scale center-surround top-hat transform through region extraction is proposed in this paper. Firstly, multi scale center-surround top-hat transform is discussed and used to extract the multi scale bright and dim image regions of the original images. Secondly, the final extracted image regions for image fusion are constructed from the extracted multi scale bright and dim image regions. Finally, after a base image is calculated from the original images, the final extracted image regions are combined into the base image through a power strategy to form the final fusion result. Because the image information of the original images are well extracted and combined, the proposed algorithm is very effective for image fusion. Comparison experiments have been performed on different image sets, and the results verified the effectiveness of the proposed algorithm.

Quadtree-based multi-focus image fusion using a weighted focus-measure

Abstract The purpose of multi-focus image fusion is integrating the partially focused images into one single image which is focused everywhere. To achieve this purpose, we propose a new quadtree-based algorithm for multi-focus image fusion. In this work, an effective quadtree decomposition strategy is presented. According to the proposed decomposition strategy, the source images are decomposed into blocks with optimal sizes in a quadtree structure. And in this tree structure, the focused regions are detected by using a new weighted focus-measure, named as the sum of the weighted modified Laplacian. Finally, the focused regions could be well extracted from the source images and reconstructed to produce one fully focused image. Moreover, the new weighted focus-measure performs better than the commonly used focus-measures on the detection of the focused regions, since it is sensitive to the homogeneous regions. The proposed algorithm is simple yet effective, because of the quadtree decomposition strategy and the new weighted focus-measure. To do the comparison, the proposed algorithm is compared with several existing fusion algorithms, in both the qualitative and quantitative ways. The experimental results show that the proposed algorithm yields good results.

Edge preserved image fusion based on multiscale toggle contrast operator

An edge preserved image fusion algorithm based on multiscale toggle contrast operator is proposed in this paper. First, the multiscale toggle contrast operator using multiscale structuring elements with the same shape and increasing sizes is discussed. Then, the multiscale dilation and erosion features which represent the edge information of the original images are extracted by using the multiscale toggle contrast operator. After the final dilation and erosion fusion features are constructed from the extracted multiscale dilation and erosion features, the final fusion image is formed by combining the final dilation and erosion fusion features into a base image calculated from the original image. Because the multiscale dilation and erosion features which represent the edge information are effectively extracted and combined into the final fusion image, clear and well preserved edge features of the final fusion image are obtained. Experimental results show that, the proposed image fusion algorithm is efficient for edge preserving and performs well.

Generalized modified atmospheric spectral model for optical wave propagating through non-Kolmogorov turbulence

A new generalized modified atmospheric spectral model is derived theoretically for wave propagating through non-Kolmogorov turbulence, which has been reported recently by increasing experimental evidence and theoretical investigation. The generalized, modified atmospheric spectrum considers finite turbulence inner and outer scales and has a spectral power law value in the range of 3 to 5 instead of the standard power law value of 11/3. When the inner scale and outer scale are set to zero and infinity, respectively, this spectral model is reduced to the classical non-Kolmogorov spectrum.

Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter

A new algorithm to enhance dim small target through modified top-hat transformation is proposed in this paper. Firstly, the property of top-hat transformation is analyzed following the property of small target regions. Secondly, a judging value is calculated following the properties of target region and top-hat transformation. Finally, the judging value is imported into the top-hat transformation to form the modified top-hat transformation. Because of the judging value in modified top-hat transformation, dim target can be significantly enhanced and heavy clutter can be effectively suppressed. Experimental results verified that the modified top-hat transformation for target enhancement under the conditions of heavy clutter and dim target intensity was effective and robust.

Irradiance scintillation for Gaussian-beam wave propagating through weak non-Kolmogorov turbulence

Kolmogorov turbulence theory based models cannot be directly applied in non-Kolmogorov turbulence case, which has been reported recently by increasing experimental evidence and theoretical investigation. In this study, based on the generalized von Karman spectral model, the theoretical expression of the irradiance scintillation index is derived for Gaussian-beam wave propagating through weak non-Kolmogorov turbulence with horizontal path. In the derivation, the expression is divided into two parts for physical analysis purpose and mathematical analysis convenience. This expression considers the influences of finite turbulence inner and outer scales and has a general spectral power law value in the range 3 to 4 instead of standard power law value of 11/3 (for Kolmogorov turbulence). Numerical simulations are conducted to investigate the influences.

Transport of intensity phase imaging from multiple noisy intensities measured in unequally-spaced planes

The noise problem is generally inevitable for phase retrieval by solving the transport of intensity equation (TIE). The noise effect can be alleviated by using multiple intensities to estimate the axial intensity derivative in the TIE. In this study, a method is proposed for estimating the intensity derivative by using multiple unevenly-spaced noisy measurements. The noise-minimized intensity derivative is approximated by a linear combination of the intensity data, in which the coefficients are obtained by solving a constrained optimization problem. The performance of the method is investigated by both the error analysis and the numerical simulations, and the results show that the method can reduce the noise effect on the retrieved phase. In addition, guidelines for the choice of the number of the intensity planes are given.

Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure

Abstract Multi-focus image fusion aims to extract the focused regions from multiple partially focused images of the same scene and then combine them together to produce a completely focused image. Detecting the focused regions from multiple images is key for multi-focus image fusion. In this paper, we propose a novel boundary finding based multi-focus image fusion algorithm, in which the task of detecting the focused regions is treated as finding the boundaries between the focused and defocused regions from the source images. According to the found boundaries, the source images could be naturally separated into regions with the same focus conditions, i.e., each region is fully focused or defocused. Then, the focused regions can be found out by selecting the regions with greater focus-measures from each pair of regions. To improve the precision of boundary detection and focused region detection, we also present a multi-scale morphological focus-measure, effectiveness of which has been verified by using some quantitative evaluations. Different from the general multi-focus image fusion algorithms, our algorithm fuses the boundary regions and non-boundary regions of the source images respectively, which helps produce a fusion image with good visual quality. Moreover, the experimental results validate that the proposed algorithm outperforms some state-of-the-art image fusion algorithms in both qualitative and quantitative evaluations.