Fusion of infrared intensity and polarization images using embedded multi-scale transform

Lin, Suzhen; Wang, Dong-juan; Zhu, Xiaohong; Zhang, Shang-Min

doi:10.1016/j.ijleo.2015.09.154

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…NSST is the most optimal multi-scales decomposition method [11][12][13][14]25]. It can effectively express image details, such as lines, edges, and contours [26].…”

Section: Fusion Enhancement Results Of a Dim Targetmentioning

confidence: 99%

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Enhancement of dim targets in a sea background based on long‐wave infrared polarisation features

Zhang

Shi

2018

IET Image Processing

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“…NSST is the most optimal multi-scales decomposition method [11][12][13][14]25]. It can effectively express image details, such as lines, edges, and contours [26].…”

Section: Fusion Enhancement Results Of a Dim Targetmentioning

confidence: 99%

“…ii. Fusion strategy for low-frequency components: The decomposed low-frequency components are appropriate parts of an image and reflect the distribution of energy [25]. For the infrared intensity image, the energy difference between a dim target and sea background is small.…”

Section: Fusion Enhancement Results Of a Dim Targetmentioning

confidence: 99%

Enhancement of dim targets in a sea background based on long‐wave infrared polarisation features

Zhang

Shi

2018

IET Image Processing

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“…Currently, for dual mode infrared image fusion, the fusion algorithm with better effect has been selected by qualitative analysis of the relationship between some known difference feature type and multiple fusion algorithms, e.g., S.Z. Lin et al [7] performance of some multiresolution transform domain methods such as DWT, SWT, CVT, CT, DTCWT, and NSCT, and builds the correspondence of these algorithms and difference features, achieving better fusion effect. T. Z.…”

Section: Literature Reviewmentioning

confidence: 99%

Set-Valued Mapping Cloud Model and its Application for Fusion Algorithm Selection of Dual Mode Infrared Images

Yang

Guo

2021

IEEE Access

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It is always a goal to fully exploit difference features and their importance in infrared polarization and intensity images to drive selection of fusion algorithms rather than using fixed algorithm, to improve pertinence and effectiveness of image fusion. However, it is difficult to obtain a better and more reasonable result for fused image due to the varied difference features and uncertain relationship between difference features and fusion algorithms. The study aims to investigate the two-tuple linguistic cloud model for possibility distribution to solve the fusion algorithm selection problem. Firstly, fusion validity distributions of difference feature amplitudes are constructed through giving consideration to both fuzziness and randomness. Secondly, in order to build set-valued mapping between difference feature amplitudes and fusion algorithms, a novel fusion validity degree transformation method into two-tuple linguistic cloud variable is proposed based on possibility theory. Thirdly, difference feature weights are calculated based on nonparametric estimation for the frequency of each corresponding feature in images. Next, a cloud weighted arithmetic averaging operator is constructed to rank the algorithms. Finally, a case of the fusion algorithm selection is provided to illustrate the implementation process and applicability of the method proposed in this paper. In addition, the proposed method can be utilized the comprehensive and multi-constrained optimal problem with clear and effective management process.

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“…Polarization is a unique property of light, and when light irradiates an object, the shape, structure, material and other information of the object can be obtained by analyzing the polarization change of light [8][9][10][11]. Since polarization imaging technology can provide both intensity and polarization information, the fusion of the intensity and polarization images through image fusion algorithms can obtain fused images with more detailed features, so the image fusion algorithms based on the intensity image and polarization image have received extensive attention and research [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

TPFNet: a tri-band polarimetric image fusion neural network

Wu,

Ma,

Chang

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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In current image fusion techniques, image fusion is usually performed on a dual-band image to obtain a fused image with significant target information, or on an intensity image and a polarization image to obtain an image with stronger visual perception. If more information is to be obtained in a single image, tri-band fusion and intensity/polarization image fusion techniques can be combined. In order to solve the above problems, in this paper, we have acquired some tri-band polarization images through a common aperture multispectral polarization photoelectric device, which contains intensity and polarization visible (VIS) images, as well as the intensity images of near-infrared (NIR) and long-wave infrared (LIR). Besides, in order to obtain good image fusion results, we built an end-to-end self-supervised image fusion network and designed an efficient loss function to train the network. We conducted experiments on TPFNet on the acquired dataset and compared it with other image fusion algorithms. The results show that TPFNet achieves excellent results in both subjective and objective evaluations.

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Fusion of infrared intensity and polarization images using embedded multi-scale transform

Cited by 14 publications

References 22 publications

Enhancement of dim targets in a sea background based on long‐wave infrared polarisation features

Enhancement of dim targets in a sea background based on long‐wave infrared polarisation features

Set-Valued Mapping Cloud Model and its Application for Fusion Algorithm Selection of Dual Mode Infrared Images

TPFNet: a tri-band polarimetric image fusion neural network

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