Jones formalism for image fusion

Khaustov, D.; Khaustov, Y. Y.; Ryzhov, Y. V.; Lychkowskyy, E.; Vlokh, R.; Nastishin, Yu. A.

doi:10.3116/16091833/22/3/165/2021

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…FIR filters of finite length have been constructed as wavelet transforms, and the same filters are used for reconstruction and decomposition. However, these filters are not symmetric, so the relationship between them is nonlinear [ 24 ]. In order to ensure a perfect refactoring, the following calculations are required:

…”

Section: Methodsmentioning

confidence: 99%

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network

2022

Computational Intelligence and Neuroscience

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With the continuous development of imaging sensors, images contain more and more information, the images presented by different types of sensors are different, and the images obtained by the same type of sensors under different parameters or conditions are also different. Multisource image fusion technology combines images acquired by different types of sensors or the same type of sensors with different parameter settings, which makes the image information more complete, compensates for the limitations of images of the same type, and also allows you to save information about the characteristics of the original image. Multimodal image mosaic and multifocal image mosaic have been studied in detail in two directions. On the one hand, a method based on frequency domain transformation is used for multiscale image decomposition. On the other hand, image extraction with neural network-based methods is proposed. The technology of convolutional neural networks (CNNs) allows to extract richer texture features. However, when using this method for fusion, it is difficult to obtain an accurate decision map, and there are artifacts in the fusion boundary. Based on this, a multifocal fusion method based on a two-stage CNN is proposed. Train the advanced intensive network to classify input image blocks as focus, and then use the appropriate merge rules to get the ideal decision tree. In addition, several versions of the fuzzy learning set have been developed to improve network performance. Experimental results show that the frames of the first stage proposed by the algorithm make it possible to obtain an accurate decision scheme and that the frames of the second stage make it possible to eliminate the pseudo-shadow of the integration boundary.

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…”

Section: Methodsmentioning

confidence: 99%

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network

2022

Computational Intelligence and Neuroscience

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Complex-scalar and complex-vector approaches for express target-oriented image fusion

et al. 2023

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Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

Zhan¹

2023

JCM

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Image fusion can extract the useful information of each channel about the same target to the maximum extent, and get high quality image. However, in this process, the image quality may be affected by noise and reduced. To reduce the image noise’s influence on the image fusion effect as well as improve the image fusion quality, a multi sensor moving image fusion analysis algorithm on the basis of neural network technology is proposed. This study designed a window adaptive strategy, use the probability density function, and built an impulse noise model, and use this model to divide each pixel in the image into noise points or signal points to obtain image impulse noise detection results, and use bilateral filtering algorithm to achieve image denoising processing; The fruit fly optimization algorithm is adopted to detect the edge of the multi sensor moving image, extract the image’s main edge points, and remove the detail edge points and noise points; nonlinear convolutional layer is used to replace most fusion layers to improve the dense network model, and the cross-entropy loss is used as the loss function in training the network, then use guided filters to generate guide maps, and generate final fusion images. According to experimental results, the noise detection method in this paper can also maintain 79.21% non-noise extraction rate under the noise density of 0.7. The highest correlation coefficient between the proposed algorithm and the standard image is 37.41. Its peak signal-to-noise ratio is as low as 0.09 and as high as 0.52. It has a minimum root mean square error of 8.52. The above values are better than other measured methods, and its edge miss rate can be as low as 1%, the image resolution is higher. It can be seen that its image denoising effect is better. Image denoising effect, and low edge missed detection rate, which effectively improves the effect of image fusion.

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Jones formalism for image fusion

Cited by 5 publications

References 27 publications

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network

Complex-scalar and complex-vector approaches for express target-oriented image fusion

Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

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