Final Fusion Result Research Articles

Application of reverse processing in SINS initial alignment and SINS/GPS integrated navigation

PurposeThe study aims to propose reverse processing solution to improve the performance of strapdown inertial navigation system (SINS) initial alignment and SINS-/global positioning system- (GPS) integrated navigation. The proposed scheme can be well applied in the fields of aircraft and aerospace navigation.Design/methodology/approachFor the SINS alignment phase, a fast initial alignment scheme is proposed: the initial value of reverse filter is determined by the final result of forward filter, and then, the reverse filter is carried out using the stored data. Multiple iterations are performed until the accuracy is satisfied. For the SINS-/GPS-integrated phase, a forward–reverse navigation algorithm is proposed: first, the standard forward filter is used, and then, the reverse filter is carried out using the initial value determined by the forward filter, and the final fusion results are achieved by the weighted smoothing of the forward and reverse filtering results.FindingsThe simulation and the actual test results show that in the initial alignment stage, the proposed reverse processing method can obviously shorten the SINS alignment time and improve the alignment accuracy. In the SINS-/GPS-integrated navigation data fusion stage, the proposed forward–reverse data fusion processing can, obviously, improve the performance of the navigation solution.Practical implicationsThe proposed reverse processing technology has an important application in improving the accuracy of navigation and evaluating the performance of real-time navigation. The proposed scheme can be not only used for SINS-/GPS-integrated system but also applied to other integrated systems for general aviation aircraft.Originality/valueCompared with the common forward filtering algorithm, the proposed reverse scheme can not only shorten alignment time and improve alignment accuracy but also improve the performance of the integrated navigation.

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.

In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images. Graphical abstract The detailed implementation of the proposed medical image fusion algorithm.

Morphology-based visible-infrared image fusion framework for smart city

Sparse representation-based approaches are often applied to image fusion. Owing to the difficulties of obtaining a complete and non-redundant dictionary, this paper proposes a hierarchical image fusion framework that applies layer-by-layer deep learning techniques to explore the detailed information of images and extract key information of images for dictionary learning. According to morphological similarities, this paper clusters source image patches into smooth, stochastic, and dominant orientation patch group. High-frequency and low-frequency components of three clustered image-patch groups are fused by max-L1 and L2-norm based weighted average fusion rule respectively. The fused low-frequency and high-frequency components are combined to obtain the final fusion results. The comparison experimentations confirm the feasibility and effectiveness of the proposed image fusion solution.

An Adaptive Fusion Algorithm for Visible and Infrared Videos Based on Entropy and the Cumulative Distribution of Gray Levels

Visible videos captured under different weather conditions may exhibit different characteristics, and thermal infrared videos are easily affected by ambient temperature variations; this sensitivity to environmental conditions makes the fusion of visible and thermal infrared videos a challenge. This paper proposes an adaptive fusion algorithm for visible and infrared videos, and uses cumulative distribution of gray levels and the entropy to adaptively retain infrared-hot targets and visible textures. The original visible and infrared frames are decomposed into two layers, namely, the base layer and the detail layer. The guided filter is employed to decompose frames due to its high efficiency. Two weight maps, one for the infrared base layer and one for the visible base layer, are adaptively generated based on the cumulative distribution of gray levels and the entropy, respectively. The visible base layer and the infrared base layer are fused based on their weight maps. The final fusion result is obtained by combining the fused base layer with the visible detail layer. Experimental results demonstrate that the proposed algorithm can achieve better fusion results compared with state-of-the-art methods.

An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

A hybrid information fusion approach to safety risk perception using sensor data under uncertainty

This paper presents a hybrid information fusion approach that integrates the cloud model and the D–S evidence theory to perceiving safety risks using sensor data under uncertainty. The cloud model provides an uncertain transforming tool between qualitative concepts and their quantitative expressions and uses the measurement of correlation to construct Basic Probability Assignments. An improved evidence aggregation strategy that combines the Dempster’ rule and the weighted mean rule is developed to get rid of counter-intuitive dilemma existing in a combination of high-conflict evidence. A three-layer information fusion framework consisting of sensor fusion, factor fusion, and area fusion is proposed to synthesize multi-source information to get the final fusion results. The developed cloud D–S approach is applied to the assessment of the safety of a real tailings dam in operation in China as a case study. Data information acquired from 28 monitoring sensors is fused in a continuous manner in order to obtain the overall safety level of the tailings dam. Results indicate that the developed approach is capable of achieving multi-layer information fusion and identifying global sensitivities of input factors under uncertainty. The developed approach proves to perform a strong robustness and fault-tolerant capacity, and can be used by practitioners in the industry as a decision tool to perceive and anticipate the safety risks in tailings dams.

A fusion estimation method based on fractional Fourier transform

Image denoising methods have different denoising performance in both spatial and transform domains, and each method has its relative advantages and inherent shortcomings compared with other methods. A very intuitive idea is to find that an effective fusion method that can combine with the advantages of different denoising methods. In this paper, we propose a novel fusion method based on the fractional Fourier transform and apply it to image denoising problem. Our method is mainly divided into three steps: Firstly, a pre-estimation is made by any two denoising method separately in the spatial domain. Secondly, using these two estimated results as well as their Fourier transform, twice Fourier transform and three times Fourier transform, we obtain a fused result in the fractional Fourier transform domain. Thirdly, the inverse fractional Fourier transform and the modulus operation are used to obtain the final fusion result. Obviously, this approach is the fusion method in four different domains. Experimental results on benchmark test images demonstrate that the proposed method outperforms state-of-the-art stand-alone methods as: BM3D, DDID, MLP, EPLL and also superior to the fusion methods such as classic wavelet fusion method, PCA fusion method and the state-of-the-art CIEM fusion method in terms of quantity value such as the peak signal to noise ratio (PSNR), the structural similarity (SSIM), and visual quality.

Signal-Level Information Fusion for Less Constrained Iris Recognition Using Sparse-Error Low Rank Matrix Factorization

Iris recognition systems working in less constrained environments with the subject at-a-distance and on-the-move suffer from the noise and degradations in the iris captures. These noise and degradations significantly deteriorate iris recognition performance. In this paper, we propose a novel signal-level information fusion method to mitigate the influence of noise and degradations for less constrained iris recognition systems. The proposed method is based on low rank approximation (LRA). Given multiple noisy captures of the same eye, we assume that: 1) the potential noiseless images lie in a low rank subspace and 2) the noise is spatially sparse. Based on these assumptions, we seek an LRA of noisy captures to separate the noiseless images and noise for information fusion. Specifically, we propose a sparse-error low rank matrix factorization model to perform LRA, decomposing the noisy captures into a low rank component and a sparse error component. The low rank component estimates the potential noiseless images, while the error component models the noise. Then, the low rank and error components are utilized to perform signal-level fusion separately, producing two individually fused images. Finally, we combine the two fused images at the code level to produce one iris code as the final fusion result. Experiments on benchmark data sets demonstrate that the proposed signal-level fusion method is able to achieve a generally improved iris recognition performance in less constrained environment, in comparison with the existing iris recognition algorithms, especially for the iris captures with heavy noise and low quality.

Infrared and Visible Image Fusion Method Based On Three Stages of Discrete Wavelet Transform

For infrared images, the performance of texture details in the target scene is not good, and for visible images, the performance is constrained by illumination and environment. An image fusion method is proposed here, its characteristic is getting three different discrete wavelet fusion methods together. The method has three stages, in the first two stages, the infrared and visible images are fused using different wavelet fusion rules, the two fusion results from the first and the second stage are fused again using another wavelet fusion rule, and then we get the final fusion result. The result proves that, compared with the similar research methods, the proposed method can ensure the quality and enhance the spatial detail of the fused image.

An evidence clustering DSmT approximate reasoning method for more than two sources

Due to the huge computation complexity of Dezert–Smarandache Theory (DSmT), its applications especially for multi-source (more than two sources) complex fusion problems have been limited. To get high similar approximate reasoning results with Proportional Conflict Redistribution 6 (PCR6) rule in DSmT framework (DSmT + PCR6) and remain less computation complexity, an Evidence Clustering DSmT approximate reasoning method for more than two sources is proposed. Firstly, the focal elements of multi evidences are clustered to two sets by their mass assignments respectively. Secondly, the convex approximate fusion results are obtained by the new DSmT approximate formula for more than two sources. Thirdly, the final approximate fusion results by the method in this paper are obtained by the normalization step. Analysis of computation complexity show that the method in this paper cost much less computation complexity than DSmT + PCR6. The simulation experiments show that the method in this paper can get very similar approximate fusion results and need much less computing time than DSmT + PCR6, especially, when the numbers of sources and focal elements are large, the superiorities of the method are remarkable.

A decision fusion method using an algorithm for fusion of correlated probabilities

ABSTRACTThis paper proposes a new decision fusion method accounting for conditional dependence (correlation) between land-cover classifications from multi-sensor data. The dependence structure between different classification results is calculated and used as weighting parameters for the subsequent fusion scheme. An algorithm for fusion of correlated probabilities (FCP) is adopted to fuse the prior probability, conditional probability, and obtained weighting parameters to generate a posterior probability for each class. A maximum posterior probability rule is then used to combine the posterior probabilities generated for each class to produce the final fusion result. The proposed FCP-based decision fusion method is assessed in land-cover classification over two study areas. The experimental results demonstrate that the proposed decision fusion method outperformed the existing decision fusion methods that do not take into account the correlation or dependence. The proposed decision fusion method can also be applied to other applications with different sensor data.

Feature-based multiexposure image-sequence fusion with guided filter and image alignment

Multiexposure fusion images have a higher dynamic range and reveal more details than a single captured image of a real-world scene. A clear and intuitive feature-based fusion technique for multiexposure image sequences is conceptually proposed. The main idea of the proposed method is to combine three image features [phase congruency (PC), local contrast, and color saturation] to obtain weight maps of the images. Then, the weight maps are further refined using a guided filter which can improve their accuracy. The final fusion result is constructed using the weighted sum of the source image sequence. In addition, for multiexposure image-sequence fusion involving dynamic scenes containing moving objects, ghost artifacts can easily occur if fusion is directly performed. Therefore, an image-alignment method is first used to adjust the input images to correspond to a reference image, after which fusion is performed. Experimental results demonstrate that the proposed method has a superior performance compared to the existing methods.

Multi-focus image fusion based on sparse feature matrix decomposition and morphological filtering

Multi-focus image fusion aims to fuse multiple images with different focus points into one single image where all pixels appear in-focus. A novel multi-focus image fusion method is presented based on a sparse feature matrix decomposition and morphological filtering. First, the sparse feature matrices of original multi-focus images are extracted by decomposing the multi-focus images. Second, a temporary matrix is obtained by weighting the sparse matrices containing salient features of original images. Third, the bright and dark regions are extracted by morphologically filtering the temporary matrix. Finally, the final fusion result is formed by importing the extracted features into the base image which is established by weighting the source images. Experimental results indicate that the proposed method not only works well in various multi-focus image fusions, but also outperforms some existing methods in both subjective and objective qualities.

Image fusion method based on bivariate alpha-stable model in multiwavelet domain

Considering statistical characteristics of multiwavelet coefficient vectors, an image fusion method with a window-based salience measure for vector fusion is proposed in the multiwavelet domain. After analyzing joint probability distribution of multiwavelet coefficient vectors, we respect the joint probability distribution as a peaky and heavy tailed two-dimensional non-Gaussian distribution, and hence model it by using a bivariate symmetric alpha-stable ( BiSαS ) distribution. Model parameters of the BiS α S distribution can be estimated from samples of multiwavelet coefficient vectors. In a sliding window, the estimated parameters can reveal the impulsive shape of the histogram of the multiwavelet coefficient vectors within the window. So we employ these model parameters to form the new salience measure indicating the degree of prominent information. In our method, vector fusion rule is introduced to avoid inconsistency of decision maps between the component sub-bands and, consequently, to lessen ringing artifacts in final fusion results. Experimental results support the effectiveness of our proposed method in the area of visual quality and objective evaluations.

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.

Image fusion through feature extraction by using sequentially combined toggle and top-hat based contrast operator

Combing the useful information of multisensor or multifocus images is important for producing effective optical images. To extract and combine the image features of the original images for image fusion well, an algorithm through feature extraction by using the sequentially combined toggle and top-hat based contrast operator is proposed in this paper. Sequentially combining toggle contrast operator and top-hat based contrast operator could be used to identify well the effective bright and dark image features. Furthermore, through multiscale extension, the effective bright and dark image features at multiscales of an image are extracted. After the final bright and dark fusion features are constructed by using the pixel-wise maximum operation on the multiscale image features from different images, the final fusion result is obtained by importing the final bright and dark fusion features into the base image. Experimental results on different types of images show that the proposed algorithm performs well for image fusion, which may be widely used in different applications, such as security surveillance, object recognition, and so on.

A Driver Fatigue Recognition Model Based on Visual Information and Bayesian Network

Driving fatigue is one of the main reasons of traffic accident. In this field, the method based on multi-source information fusion has been a leading technique. A driver fatigue recognition model based on the Bayesian network was introduced in this paper. The real-time system monitored the state of the driver's features such as eyes, mouth and head movement through computer vision technique, their results were fused with Bayesian network inference algorithm and the final fusion result could be inferred according to the probability values of different variables states. Test results showed that this method could be more reasonably robust, reliable, and accurate in fatigue detection.

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.

Multiresolution image fusion scheme based on fuzzy region feature

This paper proposes a novel region based image fusion scheme based on multiresolution analysis. The low frequency band of the image multiresolution representation is segmented into important regions, sub-important regions and background regions. Each feature of the regions is used to determine the region’s degree of membership in the multiresolution representation, and then to achieve multiresolution representation of the fusion result. The final image fusion result can be obtained by using the inverse multiresolution transform. Experiments showed that the proposed image fusion method can have better performance than existing image fusion methods.