Region based Multi-focus Image Fusion using the spectral parameter Variance

Sudheer, Allanki; Bindu, Ch. Hima

doi:10.1109/wispnet.2016.7566348

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…The pooling process takes the maximum of adjacent feature maps to reduce the feature map by a factor of four. The actual output of the sample can be calculated by (11) and (12). Assuming that the current layer is lth layer, Q P is the actual output of the sample.…”

Section: A Convolutional Neural Network Based On Transfer Learningmentioning

confidence: 99%

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Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

et al. 2019

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The development of cognitive radio and radar electronic reconnaissance has put forward an important demand for improving the recognition ability of modulated signals in complex electromagnetic environment. In this paper, we propose a valid radar signal modulation recognition technology under low signal-to-noise ratio (SNR). The recognition technology can recognize 12 different modulation signals, including Costas, LFM, NLFM, BPSK, P1-P4, and T1-T4 codes. First, we propose the image fusion algorithm of non-multi-scale decomposition to fuse images of a single signal with different time-frequency (T-F) methods. Specifically, weights are designed by the principal component analysis, which could combine significative details of T-F images. Second, we adopt transfer learning-based convolutional neural networks and self-training-based stacked autoencoder, which extract the effective information on fusion image, furthermore guarantee the recognition performance. Moreover, multi-feature fusion algorithm is used to fuse features, which reduces redundant information on features and enhances computing efficiency. Finally, the classifier is performed by a classical algorithm called support vector machine. Simulation results show that the average recognition success rate is 95.5% at SNR of −6dB. It is testified that proposed recognition technology possesses good robustness and superiority in RSR with a wide range of SNR. INDEX TERMS Radar signal modulation recognition, time-frequency images, image fusion, multi-feature fusion.

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Section: A Convolutional Neural Network Based On Transfer Learningmentioning

confidence: 99%

“…However, the results of RID with a single kernel function cannot fully interpret radar signals. Hence, the image-fusion technique proposed in [10] and [11] is applied to combine the various details of radar signal's T-F images. And then, more richer and comprehensive information of fused images is obtained.…”

Section: Introductionmentioning

confidence: 99%

Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

et al. 2019

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“…Method 5: Pixel‐level fusion scheme based on the region‐based segmentation and spectral variance discussed in Ref. .…”

Section: Resultsmentioning

confidence: 99%

“…Four image pairs out of complete CT‐MR data set is shown in Figure A,B, respectively. For presenting the comparative visual performance of the fused images, some of the methods developed previously are considered as fusion method (FM‐1), FM‐2, FM‐3, FM‐4, (mentioned above as method 4, Method 5, Method 7, and Method 9) respectively, FM‐5, and proposed MMIF method. Their fusion results are shown in Figure C‐H, respectively, and by observing these fused images, it is visualized that the resultant fused images obtained by the proposed MMIF approach have better visual ability along with better contrast and edge information that is supported by the quantitative value of the En, STD, and XEI parameters.…”

Section: Experimentation Detailsmentioning

confidence: 99%

Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain

Singh

Anand

2018

Int J Imaging Syst Tech

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Multimodal medical image fusion (MMIF) has a significant role for a better visualization of the diagnostic statistics, those help the medical professionals in the precise diagnosis of several critical diseases. This paper presents an improved fusion framework that uses the entire features extracted by the nonsubsampled shearlet transform (NSST) and adaptive biologically inspired neural model. The proposed scheme retains the required information without losing the resolution of the disease morphology. In the proposed method, the adaptive neural model based on local visibility and log Gabor energy based rules are applied to low and high-frequency components, respectively. The better fusion results obtained by the proposed approach are confirmed by a large extent of simulations on the different MR-SPECT and CT-MR neurological images. Based on all these simulated results, it states that the proposed approach is superior than the other approaches as it produces better visually fused images with improved computational measures. K E Y W O R D SCT, image fusion, MR, multimodal, SPECT

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Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain

Singh

Anand

2020

IEEE Trans. Instrum. Meas.

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Region based Multi-focus Image Fusion using the spectral parameter Variance

Cited by 3 publications

References 13 publications

Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition

Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain

Multimodal Medical Image Sensor Fusion Model Using Sparse K-SVD Dictionary Learning in Nonsubsampled Shearlet Domain

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