Current advances and future perspectives of image fusion: A comprehensive review

Karim, Shahid; Tong, Geng; Li, Jinyang; Qadir, Akeel; Farooq, Umar; Yu, Yiting

doi:10.1016/j.inffus.2022.09.019

Cited by 89 publications

(24 citation statements)

References 231 publications

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“…As shown in Table 4, compared to the spectral intensity images, the EIs of the polarization images are improved by 54.60% (@ 450 nm), 60.60% (@ 532 nm), and 60.18% (@ 632 nm), indicating the ability of polarization detection to highlight target detail [35]. Using imaging fusion [36] can enrich target or scene multidimensional information, as shown in Figure 15. Compared to the conventional intensity image, the STU pattern in the spectral image in Figure 15b can apparently be identified as a red S letter, green T letter, and blue U letter, showing the excellent ability of LCTFs to discriminate spectral components.…”

Section: Snr (Dbmentioning

confidence: 95%

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

et al. 2023

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Liquid crystal tunable filters (LCTFs) are extensively used in hyperspectral imaging systems to obtain spectral information of target scenes. However, a typical LCTF can only filter linearly polarized light, greatly reducing the transmittance of the system and limiting its application in spectral and polarization imaging. In this paper, a spectropolarimeter using Mach–Zehnder structured LCTFs (MZ-LCTFs) combined with liquid crystal variable retarders (LCVRs) is proposed. The polarized beam splitter (PBS) can make full use of the two polarization components of the incident light to improve the transmittance of the system. Specifically, the results show that the mean pixel intensity (MPI) of spectral images is improved by 93.48% compared to a typical LCTF. Subsequently, the average signal to noise ratio (SNR) of filtered and unfiltered images when simultaneously using polarization S and P channels is increased by 2.59 dB compared to a single channel. In addition, the average Standard Deviations (STDs) of DoLP and DoCP are 0.016 and 0.018, respectively. The proposed method has the potential to be applied to obtain polarization information with high optical efficiency and a full spectrum in a wide band.

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Section: Snr (Dbmentioning

confidence: 95%

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

et al. 2023

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“…Numerous image fusion methodologies have been devised to effectively combine a panchromatic (PAN) image and a multi-spectral (MS) image, resulting in an MS image that exhibits enhanced spatial and spectral resolution simultaneously. While previous research has focused mostly on the basic insight of image fusion, including the classification on the basis of the number of sensors used, processing levels, and type of information being fused [3,4,[6][7][8], this review article covers every aspect, including the prevailing state of pansharpening and spatiotemporal fusion methods for remote sensing, their real-world applications, knowledge gaps, and potential future directions. Although numerous authors have examined either pansharpening or spatiotemporal fusion in the domain of remote sensing, they have not conducted an exhaustive examination of both [5,9,10].…”

Section: Contribution Of the Surveymentioning

confidence: 99%

Pansharpening and spatiotemporal image fusion method for remote sensing

Anand,

Sharma

2024

Eng. Res. Express

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In last decades, remote sensing technology has rapidly progressed, leading to the development of numerous earth satellites such as Landsat 7, QuickBird, SPOT, Sentinel-2, and IKONOS. These satellites provide multispectral images with a lower spatial resolution and panchromatic images with a higher spatial resolution. However, satellite sensors are unable to capture images with high spatial and spectral resolutions simultaneously due to storage and bandwidth constraints, among other things. Image fusion in remote sensing has emerged as a powerful tool for improving image quality and integrating important features from multiple source images into one, all while maintaining the integrity of critical features. It is especially useful for high-resolution remote sensing applications that need to integrate features from multiple sources and hence a vital pre-processing step for various applications, including medical, computer vision, and satellite imaging. This review initially gives a basic framework for image fusion, followed by statistical analysis and a comprehensive review of various state-of-the-art image fusion methods, where they are classified based on the number of sensors used, processing levels, and type of information being fused. Subsequently, a thorough analysis of STF and pansharpening techniques for remote sensing applications has been covered, where the dataset of the DEIMOS-2 satellite is employed for evaluating various pansharpening methods while MODIS and Landsat images are employed in the spatiotemporal fusion method. A comparative evaluation of several approaches has been carried out to assess the merits and drawbacks of the current approaches. Several real-time applications of remote sensing image fusion have been explored, and current and future directions in fusion research for remote sensing have been discussed, along with the obstacles they present.

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“…Now, many multi-focus image fusion methods have been proposed. Especially, the methods based on multi-scale transform, sparse representation, edge-preserving filtering, and deep learning have achieved remarkable results in image fusion [ 16 ]. The curvelet [ 17 ], surfacelet [ 18 ], contourlet [ 19 , 20 ], and shearlet transforms [ 21 , 22 , 23 ] are widely used in multi-scale transform fields.…”

Section: Introductionmentioning

confidence: 99%

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain

Jia

et al. 2023

Sensors

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Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed. The source images are decomposed into low- and high-frequency sub-bands according to the shearlet transform. The low-frequency sub-bands are fused by sparse representation, and the high-frequency sub-bands are fused by local energy. The inverse shearlet transform is used to reconstruct the fused image. The Lytro dataset with 20 pairs of images is used to verify the proposed method, and 8 state-of-the-art fusion methods and 8 metrics are used for comparison. According to the experimental results, our method can generate good performance for multi-focus image fusion.

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Current advances and future perspectives of image fusion: A comprehensive review

Cited by 89 publications

References 231 publications

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

Pansharpening and spatiotemporal image fusion method for remote sensing

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain

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