Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection

Li, Xilai; Wang, Xiaopan; Cheng, Xiaoqi; Tan, Haishu; Li, Xiaosong

doi:10.3390/e24111527

Cited by 10 publications

(9 citation statements)

References 39 publications

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“…In B-ETEM, we employ the sum-modified Laplacian (SML) (Li et al, 2022 ) as a feature extraction algorithm for the high-frequency components; it introduces a modified Laplacian that avoids the cancelation of second-order derivatives with opposite signs in the horizontal and vertical directions. In this algorithm, Eq.…”

Section: Methodsmentioning

confidence: 99%

Multimodal brain image fusion based on error texture elimination and salient feature detection

Li¹,

Li²

2023

Front. Neurosci.

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As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM.

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Section: Methodsmentioning

confidence: 99%

Multimodal brain image fusion based on error texture elimination and salient feature detection

Li¹,

Li²

2023

Front. Neurosci.

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“…Current multi-focus image fusion methods can be essentially classified into four categories [9]: transform domain [10-13], spatial domain [14][15][16][17][18][19], sparse representation (SR) methods [20][21][22][23][24][25], and deep learning methods [26][27][28][29][30]. The spatial domain methods implement image fusion mainly by detecting the activity level of pixels or regions.…”

Section: Introductionmentioning

confidence: 99%

Fusion of full-field optical angiography images via gradient feature detection

Wang,

Li,

Tan

et al. 2024

Front. Phys.

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Full-field optical angiography (FFOA)—a real-time non-invasive imaging technique for extracting biological blood microcirculation information—contributes to an in-depth understanding of the functional and pathological changes of biological tissues. However, owing to the limitation of the depth-of-field (DOF) of optical lenses, existing FFOA imaging methods cannot capture an image containing every blood-flow information. To address this problem, this study develops a long-DOF full-field optical angiography imaging system and proposes a novel multi-focus image fusion scheme to expand the DOF. First, FFOA images with different focal lengths are acquired by the absorption intensity fluctuation modulation effect. Second, an image fusion scheme based on gradient feature detection in a nonsubsampled contourlet transform domain is developed to capture focus features from FFOA images and synthesize an all-focused image. Specifically, FFOA images are decomposed by NSCT into coefficients and low-frequency difference images; thereafter, two gradient feature detection-based fusion rules are used to select the pre-fused coefficients. The experimental results of both phantom and animal cases show that the proposed fusion method can effectively extend the DOF and address practical FFOA image defocusing problems. The fused FFOA image can provide a more comprehensive description of blood information than a single FFOA image.

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“…Due to the limited depth of field of the optical lens, the imaging device sometimes cannot achieve clear focus imaging of all objects or areas in the same scene, resulting in defocus and blurring of the scene content outside the depth of field [ 1 , 2 , 3 , 4 , 5 ]. In order to solve the above problems, multi-focus image fusion technology provides an effective way to synthesize the complementary information contained in multiple partially focused images in the same scene, and then generate an all-in-focus fusion image, which is more suitable for human observation or computer processing, and has wide application value in digital photography, microscopic imaging, holographic imaging, integrated imaging, and other fields [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

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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Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection

Cited by 10 publications

References 39 publications

Multimodal brain image fusion based on error texture elimination and salient feature detection

Multimodal brain image fusion based on error texture elimination and salient feature detection

Fusion of full-field optical angiography images via gradient feature detection

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

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