New method to quality evaluation for image fusion using gray relational analysis
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Aiming at the performance evaluation of image fusion, this paper discusses the existing criteria and the guidelines for choosing performance parameters, and then a new method is proposed to index the fusion algorithms on their fused images. In the method, each of the fused images is used to extract a comparative sequence. Meanwhile, a reference sequence is constructed by combining all the fused images involved. Then an overall performance index is obtained from the gray relational degrees between the two kinds of sequences. The interest of the method lies in the fact that it does not require a ground-truth or reference image in forming the reference sequence and is inexpensive in processing time and storage memory. Experimental results show that the method is effective, and its potential application includes intellichoice and intellectual control.Keywords:
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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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This research work for image fusion method gives an increased vividness with the definition of a variety of fusion based assessment parameters. An energy based wavelet transform (ETWT) is used to calculate features of the input images to be fused, separately, to produce a region for fusion map. Fusion intensity of each region is calculated and fusion is done, region-by-region, in the wavelet domain. This method gives results comparable to original wavelet based fusion method, HPF fusion, PCA fusion and it has a number of advantages over given methods. These include: the intelligent semantic fusion for improved perception; and for areas with certain space to be attenuated or accumulated as unfit for fusion and hence not fused. The result of any image fusion is a new generated image that has the most needed information and properties of each input image. Due to the various multi-scale transform, in the proposed fusion algorithm. The fusion has resulted to be very effective in all fusion assessment algorithms and has improved the overall gain needed for a system using image fusion for any particular need.
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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.
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The establishment of image fusion process and the optimization of image fusion method are the keys for next image fusion treatment,and which also directly determine the fusion effect and its application.A modeling method on multisensor image fusion process is analyzed and researched,and moreover a closed image fusion process taking image data as input,fusion model and fusion rule as core,fused images as output and fusion appraisal system based on multifactor integrative criterion as terminal is presented.Finally,by comparing with the results according to a certain appraisal method,a last fusion algorithm is recommended.The algorithm is more simple,practical,accordant to subjective and objective appraisal methods,and adaptive with image data and fusion purpose.
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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Fusion of images from different imaging modalities, obtained by conventional fusion methods, may cause artifacts, including destructive superposition and brightness irregularities, in certain cases. This paper proposes two methods for improving image multimodal fusion quality. Based on the finding that a better fusion can be achieved when the images have a more positive correlation, the first method is a decision algorithm that runs at the preprocessing fusion stage and determines whether a complementary gray level of one of the input images should be used instead of the original one. The second method is suitable for multiresolution fusion, and it suggests choosing only one image from the lowest-frequency sub-bands in the pyramids, instead of combining values from both sub-bands. Experimental results indicate that the proposed fusion enhancement can reduce fusion artifacts. Quantitative fusion quality measures that support this conclusion are shown.
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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.
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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.
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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.
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