Multi-focus image fusion through pixel-wise voting and morphology

Luo, Huibin; KinTak, U; Zhao, Weikang

doi:10.1007/s11042-022-13218-y

Cited by 4 publications

(2 citation statements)

References 34 publications

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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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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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“…The pixel-level image fusion method carries out a series of operations on pixels, and it can avoid the information loss of image reconstruction, which is conducive to subsequent processing. Thus, the pixel-level image fusion method has been most widely researched [10][11][12][13]. The pixel-level image fusion method of a scene can be divided into three categories: multi-focus image fusion methods in spatial domain, transform domain and deep learning [1,8,9].…”

Section: Introductionmentioning

confidence: 99%

An unsupervised multi‐focus image fusion method based on Transformer and U‐Net

Jin

Zhou

et al. 2022

IET Image Processing

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This work presents a multi‐focus image fusion method based on Transformer and U‐Net with an unsupervised training fashion. In this work, the authors introduce Transformer into image fusion because it has great ability to capture the global dependencies and low‐frequency features. In image processing, convolutional neural network (CNN) has good performance of detailed feature extraction but a weakness for global feature extraction, and Transformer has limited power in local or detailed information extraction but a strong capacity for global feature extraction. Thus, this work combines the advantages of CNN and Transformer to propose an unsupervised decision map making model for image fusion joint U‐Net. The authors construct a model including feature extraction and feature reconstruction modules which correspond to the encoder and decoder network of U‐Net, respectively. In addition, perceptual loss is introduced on the basis of structural similarity loss because the combination of these two loss functions can achieve better performance with lower training cost. Experiments show that the proposed image fusion method performs better fusion performance compared with the existing methods.

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