A Comparative Study of Various Digital Image Fusion Techniques: A Review

Kaur, Prabhdeep; Kaur, Mandeep

doi:10.5120/19968-1826

Cited by 29 publications

(36 citation statements)

References 20 publications

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“…Multi-focus image fusion aims to acquire an all-in-focus image, which can be used in diverse tasks of image processing [17]. From the literature, multi-focus image fusion (MFIF) methods can be divided into 3 categories: spatial domain, transform domain and neural network [18].…”

Section: Related Workmentioning

confidence: 99%

Fast Multi-Focus Image Fusion Using Inner Channel Prior

Kuang

2021

IEEE Access

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In this paper, we propose a simple but effective sharpness prior-inner channel prior-to detect the focused area of multi-focus images. The inner channel prior is a kind of location feature of natural image sharpness. It is based on a key observation-the high-frequency information of the saturation channel is located inside the object. The inner channel prior indicates the focus degree inside objects, which can obtain better color fidelity and sharper object. Using the proposed prior combined with the multi-scale image fusion, we can directly obtain a sharp image with an extended depth of field focusing on all the objects. Results on a variety of defocused image sequences demonstrate the availability of the proposed prior. In addition, the proposed prior is insensitive to overexposure and can better maintain the color of multi-focus images.INDEX TERMS Image fusion, Multi-focus images, Focus measurement, Depth of field, Multi-scale transform.

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Section: Related Workmentioning

confidence: 99%

Fast Multi-Focus Image Fusion Using Inner Channel Prior

Kuang

2021

IEEE Access

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“…We are defining m, n, d and e matrices as below: and (6) q=m.b.n , p=d.b.e (7) Energy of Laplacian (4), as the sum of entry-wise products of the elements, can be rewritten by the trace of a product by (8).…”

Section: A Eol Calculation In Dct Domainmentioning

confidence: 99%

“…Several multi-focus image fusion researches have been done in the spatial domain [4][5][6][7][8][9][10][11][12][13][14]. The simplest methods in the spatial domain used a weighted arithmetic mean of the source image pixels intensity [14].…”

Section: Introductionmentioning

confidence: 99%

Multi-focus image fusion using Singular Value Decomposition in DCT domain

Amin-Naji

Ranjbar-Noiey

Aghagolzadeh

2017

2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP)

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The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multifocused images are captured with different depths of focus of cameras. Multi-focus image fusion is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks (VSN). The previous works in DCT domain have some errors in selection of the suitable divided blocks according to their criterion for measurement of the block contrast. In this paper, we used variance of Laplacian (VOL) and energy of Laplacian (EOL) as criterion to measure the contrast of image. Also in this paper, the EOL and VOL calculations directly in DCT domain are prepared using vector processing. We developed four matrices which calculate the Laplacian of block easily in DCT domain. Our works greatly reduce error due to unsuitable block selection. The results of the proposed algorithms are compared with the previous algorithms in order to demonstrate the superiority of the output image quality in the proposed methods. The several JPEG multi-focus images are used in experiments and their fused image by our proposed methods and the other algorithms are compared with different measurement criteria.

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“…In [5], [6], the authors introduced a review study on medicinal imaging fusion approaches used in the health system to provide an overview of the fusion methods utilized in the applications of medical services, like curvature transformation, waveform, contour transformation, standing waveform transformation, and frame transformation.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

et al. 2021

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The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.INDEX TERMS Medical imaging, fusion process, imaging modalities, dual-tree complex wavelet transform, curvelet transform, discrete wavelet transform, principal component analysis.

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A Comparative Study of Various Digital Image Fusion Techniques: A Review

Cited by 29 publications

References 20 publications

Fast Multi-Focus Image Fusion Using Inner Channel Prior

Fast Multi-Focus Image Fusion Using Inner Channel Prior

Multi-focus image fusion using Singular Value Decomposition in DCT domain

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

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