Multi-focus image fusion based on multi-scale sparse representation

Ma, Xiaole; Wang, Zhihai; Hu, Shaohai

doi:10.1016/j.jvcir.2021.103328

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…The algorithm is divided into three sections: creating the dictionary, representing the image, integrating, and reconstructing. [36] Adaptively extracting source image patches from source image patches is done using the K-SVD [37]algorithm. To preserve each source image signal, a joint dictionary is built, and the batch-OMP algorithm is used to estimate the coefficient vectors.…”

Section: Sparse Representationmentioning

confidence: 99%

Exploring Types of Multi-Focus Image Fusion

Ghazal,

Majjed Dahl

2023

IRJIET

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This paper explains methods. Fusion of multiplefocus images can actually take care of the profundity of field issue in optical focal point regions, the blurred picture appears strange due to the high frequency degradation Information. Most often, the camera is to blame for this. The absence of a deep field is caused by optics in the cameras. The picture becomes sharper as a result. Only in particular locations for a comprehensive focal length image, Fusion of multiple-focus images primary objective is to solve a problem with depth of field cameras. By blending at least two to some degree centred pictures into a solitary totally centred picture, Combination of various centre pictures can tackle the optical focal point's profundity of field issue.

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Section: Sparse Representationmentioning

confidence: 99%

Exploring Types of Multi-Focus Image Fusion

Ghazal,

Majjed Dahl

2023

IRJIET

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“…Along with these applications, different terms have been used to indicate the different techniques, such as multisensor image fusion for remote sensing (Abdikan et al, 2014), multi-model image fusion for medical diagnosis (Hermessi et al, 2021), to the multi-focus image for optical microscopy (Liu et al, 2020). Meanwhile, more and more advanced techniques have been integrated into the fusion algorithms, such as sparse representation (Ma et al, 2021, Zhang et al, 2021, deep learning (Mustafa et al, 2020, Li andWu, 2018). While the fast development of image fusion algorithms, the development of fusion quality assessment, has been received less attention.…”

Section: Introductionmentioning

confidence: 99%

Critical Reflection on Quantitative Assessment of Image Fusion Quality

Ehlers²

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Image fusion technique has been extended its development from multi-sensor fusion, multi-model fusion to multi-focus fusion. More and more advanced techniques such as deep learning have been integrated into the development of image fusion algorithms. However, as an important aspect, fusion quality assessment has been received less attention. This paper intends to reflect on the commonly used indices for quantitative assessment and investigate how they can represent the fusion quality regarding spectral preservation and spatial improvement. We found that image dissimilarities are unavoidable due to the spectral coverage of different image sensors. Image fusion should integrate these dissimilarities when they are representing spatial improvement. Such integration will naturally change the pixel values. However, as the quality indices for the assessment of spectral preservation are measuring image dissimilarities, the integration of spatial information will lead to a low fusion quality assessment. For the evaluation of spatial improvement, the quality indices only work if the spatial details have been lost; however, in the case of spatial details gain, these indices do not reflect them as spatial improvements. Moreover, this paper raises attention to image processing procedures involved in image fusion, including image geo-registration, image clipping and image resampling, which will change image statistics and thereby influence the quality assessment when statistical indices are used.

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DFANet: A Dual-Stream Deep Feature Aware Network for Multi-focus Image Fusion

Dong,

Zhao,

et al. 2024

Lecture Notes in Computer Science

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Multi-focus image fusion based on multi-scale sparse representation

Cited by 9 publications

References 46 publications

Exploring Types of Multi-Focus Image Fusion

Exploring Types of Multi-Focus Image Fusion

Critical Reflection on Quantitative Assessment of Image Fusion Quality

DFANet: A Dual-Stream Deep Feature Aware Network for Multi-focus Image Fusion

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