Fusion of Panchromatic and Multispectral Images via Morphological Operator and Improved PCNN in Mixed Multiscale Domain

Jiao, Jiao; Wu, Lei

doi:10.1109/prrs.2018.8486292

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…As the third generation of artificial neural network, the Pulse Coupled Neural Network (PCNN) creates fusion of the neuron neighborhood capturing feature, the pulse coupling feature, the nonlinear multiplication modulating feature and the exponential decay threshold mechanism [29]. At the same time, because of its synchronous pulse distribution feature and capturing feature, the PCNN model is applied to the field of image processing [30]- [32]. Because the traditional PCNN model is sensitive to parameters and it is very difficult to properly select and adjust the parameters, the traditional PCNN model is complex and has high time complexity; Therefore, at present, theoretical research and practical application of the PCNN model mainly focus on model improvement and obtaining parameters [33].…”

Section: Adaptive Pcnn Model Based On Directional Region Energy mentioning

confidence: 99%

The PAN and MS Image Pansharpening Algorithm Based on Adaptive Neural Network and Sparse Representation in the NSST Domain

Wang

Bai

Li³

et al. 2019

IEEE Access

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How to improve the spatial resolution as much as possible while maintaining the spectral information of multi-spectral (MS) image in the field of image fusion is of great significance for practical applications, such as map updating, feature classification, and target recognition. To analyze the coefficients of the subband distribution characteristics, in this paper, we propose a new panchromatic (PAN) and MS image pansharpening model based on an adaptive neural network and sparse representation in the non-subsample shearlet transform (NSST) domain. First, this algorithm is specific to regional directional characteristics in the high-frequency subband of PAN and MS images, and we propose an adaptive pulse coupled neural network (PCNN) model. The model can adaptively calculate the link strength of a neural cell based on the region energy. Furthermore, we apply the model to the high-frequency fusing process with the corresponding fusion rule, and the rule can distinguish the high-frequency coefficients by ignition times, which can more effectively capture the geometric texture information and detailed information in the PAN image, enhancing the spatial resolution of the fused image. Second, because of the low-frequency sub-bands from the PAN image and I component obtained by intensity-hue-saturation (IHS) transformation of the MS images with high similarity to the original image but poor sparsity, we select a set of PAN images for learning, a more targeted over-complete dictionary for low-frequency sub-band sparse representation is obtained. Then, the larger absolute value of the sparse matrix is selected to obtain the low-frequency coefficients for the fusion image while maintaining the MS spectral information effectively, and the representation of characteristic information of low-frequency subband is more effective. A large number of simulation experiments verify the effectiveness of the proposed method.

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Section: Adaptive Pcnn Model Based On Directional Region Energy mentioning

confidence: 99%

The PAN and MS Image Pansharpening Algorithm Based on Adaptive Neural Network and Sparse Representation in the NSST Domain

Wang

Bai

Li³

et al. 2019

IEEE Access

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“…The test results demonstrate that the proposed technique outflanks alternate strategies in visual impact and goal evaluations. [1] Z. Fan, L. Yan, On account of the poor lighting conditions during the evening, noticeable images are frequently fused with comparing infrared images for improvement of the scenes in night vision.…”

Section: Literature Surveymentioning

confidence: 99%

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

Kumari¹,

Rawat²

2018

IJCA

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Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc. For fusing the image various techniques has been proposed by different previous works such as wavelet transform, IHS (Intensity, Hue and Saturation) and Principal Component Analysis (PCA), based methods etc. In this paper literature of the image fusion is discussed with implementation using wavelet transform used for the specific application as in the image restoration field. Using Image fusion may improve the perceptual quality of the restored images. Usually image deblurring methods are used at the front end for restoration and then image fusion is used for improving the visual quality. Paper uses three de-blurring technique to blindly restoring the image then use statistical parameters for adopting the best fused images out of various hybrid fusion results. Performance is tested on images with distinct features.

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Fusion of Panchromatic and Multispectral Images via Morphological Operator and Improved PCNN in Mixed Multiscale Domain

Cited by 2 publications

References 13 publications

The PAN and MS Image Pansharpening Algorithm Based on Adaptive Neural Network and Sparse Representation in the NSST Domain

The PAN and MS Image Pansharpening Algorithm Based on Adaptive Neural Network and Sparse Representation in the NSST Domain

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

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