Visible and infrared image fusion using DTCWT and adaptive combined clustered dictionary

Aishwarya, N.; Thangammal, C. Bennila

doi:10.1016/j.infrared.2018.08.013

Cited by 40 publications

(9 citation statements)

References 25 publications

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“…In this proposed methodology, DSWT was utilized to decompose the significant features of the source images into a series of sub-images, each with different levels and spatial frequencies; moreover, DCT was applied to separate the Journal of Computer and Communications important details of the sub-images on the basis of their different energy frequencies while LSF was employed to enhance the regional features of DCT coefficients that could be useful for image feature extraction [45]. The work of [84] further proposed a new method for fusing visible and infrared images, referred to as DTCWT-ACCD, based on DTCWT and an adaptive combined clustered dictionary. Yang et al put forward a novel remote sensing image fusion method based on adaptively weighted joint detail injection.…”

Section: Combination Of Different Transformsmentioning

confidence: 99%

Multi-Sensor Image Fusion: A Survey of the State of the Art

Li¹,

Yu²,

Zhang³

et al. 2021

JCC

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Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

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Section: Combination Of Different Transformsmentioning

confidence: 99%

Multi-Sensor Image Fusion: A Survey of the State of the Art

Li¹,

Yu²,

Zhang³

et al. 2021

JCC

View full text Add to dashboard Cite

show abstract

“…DTCWT is an extension of DWT and has translation invariance. Aishwarya et al [ 22 ] proposed an image fusion method utilizing DTCWT and adaptive combined clustered dictionary, leading to good performance than the conventional multiscale transform-based algorithms and the state-of-the-art sparse representation-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

2021

Entropy

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Multimodal medical image fusion aims to fuse images with complementary multisource information. In this paper, we propose a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-subsampled contourlet transform (NSCT) domain. Firstly, the source images are decomposed by NSCT, several low- and high-frequency sub-bands are generated. Secondly, the PCNN-based fusion rule is used to process the low-frequency components, and the GIF-WSEML fusion model is used to process the high-frequency components. Finally, the fused image is obtained by integrating the fused low- and high-frequency sub-bands. The experimental results demonstrate that the proposed method can achieve better performance in terms of multimodal medical image fusion. The proposed algorithm also has obvious advantages in objective evaluation indexes VIFF, QW, API, SD, EN and time consumption.

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“…Iqbal et al [ 12 ] introduced a multifocus image fusion approach using a DWT and a guided image filter to improve the definition of the fused images. Aishwarya et al [ 13 ] used a DTCWT and an adaptive combined clustered dictionary for visible and infrared image fusion to enhance the target information. Wang et al [ 14 ] proposed a multispectral (MS) and panchromatic (PAN) image fusion technique based on the hidden Markov tree model in a complex tight framelet transform domain to improve the spatial resolution of the MS image while keeping the spectral information.…”

Section: Introductionmentioning

confidence: 99%

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

2021

Sensors

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The rapid development of remote sensing and space technology provides multisource remote sensing image data for earth observation in the same area. Information provided by these images, however, is often complementary and cooperative, and multisource image fusion is still challenging. This paper proposes a novel multisource remote sensing image fusion algorithm. It integrates the contrast saliency map (CSM) and the sum-modified-Laplacian (SML) in the nonsubsampled shearlet transform (NSST) domain. The NSST is utilized to decompose the source images into low-frequency sub-bands and high-frequency sub-bands. Low-frequency sub-bands reflect the contrast and brightness of the source images, while high-frequency sub-bands reflect the texture and details of the source images. Using this information, the contrast saliency map and SML fusion rules are introduced into the corresponding sub-bands. Finally, the inverse NSST reconstructs the fusion image. Experimental results demonstrate that the proposed multisource remote image fusion technique performs well in terms of contrast enhancement and detail preservation.

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Visible and infrared image fusion using DTCWT and adaptive combined clustered dictionary

Cited by 40 publications

References 25 publications

Multi-Sensor Image Fusion: A Survey of the State of the Art

Multi-Sensor Image Fusion: A Survey of the State of the Art

Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

Saliency-Guided Nonsubsampled Shearlet Transform for Multisource Remote Sensing Image Fusion

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