A novel multi-focus image fusion method for improving imaging systems by using cascade-forest model

He, Lin; Yang, Xiaobo; Lu, Lu; Wu, Wei; Ahmad, Awais; Jeon, Gwanggil

doi:10.1186/s13640-020-0494-8

Cited by 12 publications

(7 citation statements)

References 31 publications

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“…However, these images are not included in the paper because the techniques that are contrasted with the proposed approach do not produce outcomes for these images. Desk, balloon, book, clock, flower, lab, leaf, leopard, flowerpot, Pepsi, wine, and craft images are used to compare methodologies in the literature with [21][22][23][24][25][26][27][28]30]. In addition, the outcomes of the proposed technique for certain tried source images were presented.…”

Section: Resultsmentioning

confidence: 99%

“…Lin He, and colleagues proposed a MIF approach for improving imaging systems. The cascade forest was incorporated into MIF to estimate the influence of fusion rules [23]. Samet Aymaz et al proposed a unique MIF approach based on a super-resolution hybrid method [24].…”

Section: Introductionmentioning

confidence: 99%

“…Chenglang Zhang [32] proposed a novel MIF approach based on multiscale transform (MST) and convolution sparse representation (CSR) to address the inherent defects of both the MST and SR-based fusion methods. The proposed approach is put up against the approaches discussed in the literature [21][22][23][24][25][26][27][28]30].…”

Section: Introductionmentioning

confidence: 99%

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Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

Mohan

Chouhan²,

Rout

et al. 2022

Applied Sciences

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Multi-focus image fusion (MIF) uses fusion rules to combine two or more images of the same scene with various focus values into a fully focused image. An all-in-focus image refers to a fully focused image that is more informative and useful for visual perception. A fused image with high quality is essential for maintaining shift-invariant and directional selectivity characteristics of the image. Traditional wavelet-based fusion methods, in turn, create ringing distortions in the fused image due to a lack of directional selectivity and shift-invariance. In this paper, a classical MIF system based on quarter shift dual-tree complex wavelet transform (qshiftN DTCWT) and modified principal component analysis (MPCA) in the laplacian pyramid (LP) domain is proposed to extract the focused image from multiple source images. In the proposed fusion approach, the LP first decomposes the multi-focus source images into low-frequency (LF) components and high-frequency (HF) components. Then, qshiftN DTCWT is used to fuse low and high-frequency components to produce a fused image. Finally, to improve the effectiveness of the qshiftN DTCWT and LP-based method, the MPCA algorithm is utilized to generate an all-in-focus image. Due to its directionality, and its shift-invariance, this transform can provide high-quality information in a fused image. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques in terms of visual and quantitative evaluations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

Mohan

Chouhan²,

Rout

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The developed system combines the probability productions of the features from such modalities in real environment through a decision-based fusion technique that is further fed to collaborative classifier. However, it is very much difficult to sustain the system an admirable classification result [35], [36]. In a more recent work, the authors of [37] proposed a hybrid feature selection method that is an integration of a filter and wrapper methods.…”

Section: Related Workmentioning

confidence: 99%

A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

et al. 2021

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Context-awareness is an essential part of pervasive computing. Video-based human activity recognition (HAR) has arisen as an imperative module to detect user's situation for involuntary facility delivery in context-aware domains. The activity recognition systems are frequently employed for protective and practical health care. Most of the existing works utilize RGB (red, green, and blue) cameras which present confidentiality and security concerns in the health-care domain. The existing approaches also do not sustain their performance results under the presence of a depth camera. Moreover, the accuracy of an HAR system relies on the extraction and selection of the prominent features from the feature space. To address these limitations, in this research, we first employ a depth camera to resolve the confidentiality and security concerns, and propose an unsupervised segmentation algorithm that can accurately segment the human body from the video frame. Then, we propose a new feature selection technique that has the ability to excerpt and select the best features from the feature space. Our proposed feature selection method focuses on the selection of confined features from the series of images and discriminate their category based on reversion (i.e., regression) value. The proposed method extracts and selects the best features through the benefits of forward selection and backward regression algorithms. Finally, we have trained and tested our proposed system by employing hidden Markov model (HMM) to label the activities. The proposed approach presents a significant performance against the existing works using depth camera.

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“…If one object in the scene is in focus, another one will be out of focus, therefore, a fusion process is required to achieve all objects in focus and all focused objects are selected [1,2]. The multi-focus image fusion is a sub-domain of image fusion, and it is the basis of many fusion fields such as digital imaging, medical imaging, remote sensing, and machine vision [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

Abas¹,

Baykan²

2021

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Focus is limited and singular in many image capture devices. Therefore, different focused objects at different distances are obtained in a single image taken. Image fusion can be defined as the acquisition of multiple focused objects in a single image by combining important information from two or more images into a single image. In this paper, a new multi-focus image fusion method based on Bat Algorithm (BA) is presented in a Multi-Scale Transform (MST) to overcome limitations of standard MST Transform. Firstly, a specific MST (Laplacian Pyramid or Curvelet Transform) is performed on the two source images to obtain their low-pass and high-pass bands. Secondly, optimization algorithms were used to find out optimal weights for coefficients in low-pass bands to improve the accuracy of the fusion image and finally the fused multi-focus image is reconstructed by the inverse MST. The experimental results are compared with different methods using reference and non-reference evaluation metrics to evaluate the performance of image fusion methods.

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A novel multi-focus image fusion method for improving imaging systems by using cascade-forest model

Cited by 12 publications

References 31 publications

Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

Improved Procedure for Multi-Focus Images Using Image Fusion with qshiftN DTCWT and MPCA in Laplacian Pyramid Domain

A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

Multi-Focus Image Fusion with Multi-Scale Transform Optimized by Metaheuristic Algorithms

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