CASPFuse: An Infrared and Visible Image Fusion Method based on Dual-cycle Crosswise Awareness and Global Structure-tensor Preservation
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Sensor Fusion
For the spectral and spatial limitation of a single image sensor,this paper presented a new method on infrared and visible image fusion,which was based on the cooperative work of several image fusion algorithms.In the method,the quality of fused images gained from the different fusion algorithms were indexed first. And then multiple dynamic image fusions were implemented by the mechanism of competition,cooperation,adjustment and feedback among the fusion algorithms.Thus a fusion image was acquired which not only had stable performance,but also represented the best fusion effect.Pilot experimental results show that the proposed method can provide with a high quality fused-image.
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Abstract In the case of multi-sensors weighted data fusion with unknown prior knowledge, a weighted data fusion method based on measurement traversal correction is proposed. The fusion accuracy of multi-sensors data fusion is influenced by both the measurement data accuracy and the data fusion weights of sensors. The measurement data of sensors is corrected through analyzing the reliability of different time data measured by sensors. The fusion weights of multi-sensors data fusion are optimal through depply analyzing the influence of weight distribution on multi-sensors data fusion accuracy. Typical examples are used to validate the proposed fusion algorithm, and the result shows that the fusion result is satisfying, and the algorithm is theoretical and practical.
Sensor Fusion
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In order to improve the fusion quality of SAR and multi-spectral image, this paper proposes an image fusion method based on nonsubsampled contourlet transform (NSCT) and IHS transform. Since the fusion rule plays a very important role during the fusion process, four fusion rules are analyzed and compared. Three fusion rules are commonly used in previous works and a new fusion rule is proposed in this paper. To evaluate the performance of different fusion rules, fusion experiments are carried on COSMO-SkyMed SAR and Landsat OLI image. The experimental results indicate that the proposed rule is more effective than the other three regular fusion rules.
Contourlet
Fusion rules
Rule-based system
Sensor Fusion
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Recent years have witnessed wide application of infrared and visible image fusion. However, most existing deep fusion methods focused primarily on improving the accuracy without taking much consideration of efficiency. In this paper, our goal is to build a better, faster and stronger image fusion method, which can reduce the computation complexity significantly while keep the fusion quality unchanged. To this end, we systematically analyzed the image fusion accuracy for different depth of image features and designed a lightweight backbone network with spatial frequency for infrared and visible image fusion. Unlikely previous methods based on traditional convolutional neural networks, our method can greatly preserve the detail information during image fusion. We analyze the spatial frequency strategy of our prototype and show that it can maintain more edges and textures information during fusion. Furthermore, our method has fewer parameters and lower computation in comparison of state-of-the-art fusion methods. Experiments conducted on benchmarks demonstrate that our method can achieve compelling fusion results over 97.0% decline of parameter size, running 5 times faster than state-of-the-art fusion methods.
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An enhancement method of the multifocus images fusion result is proposed. It is a two step approach: first a classic fusion method is applied and a fusion map is built, indicating the weight of each input image in the fusion process. The fusion map is a binary or gray level image. In a second step, because it is noisy, this image is filtered. The fused image obtained in the first step is rebuilt based on the filtered fusion map. The proposed method was tested together with two classic fusion methods: multifocus image fusion using morphological wavelets and information level based multifocus image fusion.
Sensor Fusion
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We proposed an image fusion method based on cognition for infrared and visible images. Multi-source image fusion is guided top-down by using the prior knowledge as acquired from deep understanding of multi-source images. As different fusion methods and fusion rules are used for contents of different importance, it makes the image fusion process more free and flexible. Experiments for the presented method are conducted by using infrared and visible image. The fusion results and the image fusion quality indexes are compared with those of the traditional region-based fusion methods. The comparison show the proposed method is far superior to the traditional region-based fusion methods.
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Some subjects of image fusion based on wavelet transform are discussed. Firstly, the method of selecting wavelet bases in image fusion is proposed, and the principle and formula on phase adjustment are deduced. Secondly, a new fusion rule of high frequency channel is presented. Based on the rule, a series of experiments are made on PC. The experimental result indicates that the algorithm and fusion rule are very suitable for multi\|resource image fusion, especially for multi\|spectral image fusion.
Fusion rules
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The purpose of data fusion is to produce an improved model or estimate of a system from a set of independent data sources. There are various multisensor data fusion approaches, of which Kalman filtering is one of the most significant. Methods for Kalman filter based data fusion include measurement fusion and state fusion. This paper gives a simple a review of fusion and state fusion, and secondly proposes new integrated method of state fusion based on fusion procedures at the prediction and update level. To illustrate application, a simple example is performed to evaluate the proposed method.
Sensor Fusion
Data set
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Sensor Fusion
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Medical image fusion is being used at large by clinical professionals for improved diagnosis and treatment of diseases. The main aim of image fusion process is to combine complete information from all input images into a single fused image. Therefore, a novel fusion rule is proposed for fusing medical images based on Daubechies complex wavelet transform (DCxWT). Input images are first decomposed using DCxWT. The complex coefficients so obtained are then fused using normalized correlation based fusion rule. Finally, the fused image is obtained by inverse DCxWT with all combined complex coefficients. The performance of the proposed method has been evaluated and compared both visually and objectively with DCxWT based fusion methods using state-of art fusion rules as well as with existing fusion techniques. Experimental results and comparative study demonstrate that the proposed fusion technique generates better results than existing fusion rules as well as with other fusion techniques.
Fusion rules
Complex wavelet transform
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