Technique for image fusion based on NSST domain improved fast non-classical RF

Kong, Weiwei; Liu, Jianping

doi:10.1016/j.infrared.2013.06.009

Cited by 18 publications

(7 citation statements)

References 23 publications

(40 reference statements)

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“…NSST-based method with nonclassic receptive field-based fusion rule [18] denoted as M1. Improved intersecting cortical model-based fusion rule adapted in NSST domain [26] denoted as M2.…”

Section: Methods Used For Comparisonmentioning

confidence: 99%

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Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain

Ganasala

Kumar

2015

J Digit Imaging

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Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

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“…NSST-based method with nonclassic receptive field-based fusion rule [18] denoted as M1. Improved intersecting cortical model-based fusion rule adapted in NSST domain [26] denoted as M2.…”

Section: Methods Used For Comparisonmentioning

confidence: 99%

“…Directional vector norm and band-limited contrast-based fusion rules in NSST domain have been proposed in [17]. Non-classical RF model-based fusion rules in NSST domain have been tested in [18]. Fusion outcome of all these methods suffered from poor contrast and loss of information related to one of the source images.…”

Section: Introductionmentioning

confidence: 99%

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain

Ganasala

Kumar

2015

J Digit Imaging

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“…Another benefit of this implementation is the application of a non‐subsampled convolution, which has been shown to be very effective in denoising applications. Figure b illustrates the multi‐scaling decomposition with

L = 2

levels and corresponding multi‐directional decomposition of NSST (Kong and Liu ). NSP will produce

L + 1

frequency components, including one low‐frequency component and L high‐frequency components.…”

Section: Theorymentioning

confidence: 99%

“…At present, there are some research results in the field of image processing such as image denoising (Qi ; Yang et al . ; Shahdoosti and Khayat ), image fusion (Kong and Liu ), and target edge detection (Yi et al . ) by NSST.…”

Section: Introductionmentioning

confidence: 99%

“…NSST can satisfy the requirement of the translation invariance. At present, there are some research results in the field of image processing such as image denoising (Qi 2013;Yang et al 2014;Shahdoosti and Khayat 2016), image fusion (Kong and Liu 2013), and target edge detection (Yi et al 2008) by NSST. The research results show that the NSST can preserve the image information, remove the random noise, and improve the SNR effectively.…”

Section: Introductionmentioning

confidence: 99%

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Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition

Liang

Zhang

2018

Geophysical Prospecting

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The existence of strong random noise in surface microseismic data may decrease the utility of these data. Non‐subsampled shearlet transform can effectively suppress noise by properly setting a threshold to the non‐subsampled shearlet transform coefficients. However, when the signal‐to‐noise ratio of data is low, the coefficients related to the noise are very close to the coefficients associated with signals in the non‐subsampled shearlet transform domain that the coefficients related to the noise will be retained and be treated as signals. Therefore, we need to minimise the overlapping coefficients before thresholding. In this paper, a singular value decomposition algorithm is introduced to the non‐subsampled shearlet transform coefficients, and low‐rank approximation reconstructs each non‐subsampled shearlet transform coefficient matrix in the singular value decomposition domain. The non‐subsampled shearlet transform coefficients of signals have bigger singular values than those of the random noise, which implies that the non‐subsampled shearlet transform coefficients can be well estimated by taking only a few largest singular values. Therefore, those properties of singular value decomposition may significantly help minimise overlapping of noise and signals coefficients in the non‐subsampled shearlet transform domain. Finally, the denoised microseismic data are obtained easily by giving a simple threshold to the reconstructed coefficient matrix. The performance of the proposed method is evaluated on both synthetic and field microseismic data. The experimental results illustrate that the proposed method can eliminate random noise and preserve signals of interest more effectively.

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Technique for Image Fusion Based on PCNN and Convolutional Neural Network

Kong

Lei

2017

Advances in Internetworking, Data &Amp; Web Technologies

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Technique for image fusion based on NSST domain improved fast non-classical RF

Cited by 18 publications

References 23 publications

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain

Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition

Technique for Image Fusion Based on PCNN and Convolutional Neural Network

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