Multi-focus image fusion based on probability filtering and region correction

Xia, Xiaohua; Ya, Yao; Yin, Lijuan; Wu, Shida; Li, Haochen; Yang, Zhengxian

doi:10.1016/j.sigpro.2018.07.004

Cited by 17 publications

(4 citation statements)

References 32 publications

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“…This approach effectively preserved the edges of the decision maps by utilizing the non-Sub sampled Shearlet Transform and KN earest Neighbors matting. Following this, Li et al developed a novel MFIF method that combined spatial and transform-domain techniques [7].…”

Section: Hybrid Methodsmentioning

confidence: 99%

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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: Hybrid Methodsmentioning

confidence: 99%

“…Xia et al". (2018) [7]presented a strategy for characterizing misidentified pixels into bunched and scattered bunches utilizing likelihood separating and locale revision, bringing about proficient rectification of these misidentified pixels. [8].…”

Section: Pixel Basedmentioning

confidence: 99%

Exploring Types of Multi-Focus Image Fusion

Ghazal,

Majjed Dahl

2023

IRJIET

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“…The development of sensor technology has promoted the expansion of image processing in many applications [49,53]. Because of the limitation of depth of field (DOF) in sensor imaging, the optical elements can only capture the focused images of part of the DOF scene [2].…”

Section: Introductionmentioning

confidence: 99%

Focus-pixel estimation and optimization for multi-focus image fusion

Gong

2022

Multimed Tools Appl

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To integrate the effective information and improve the quality of multi-source images, many spatial or transform domain-based image fusion methods have been proposed in the field of information fusion. The key purpose of multi-focus image fusion is to integrate the focused pixels and remove redundant information of each source image. Theoretically, if the focused pixels and complementary information of different images are detected completely, the fusion image with best quality can be obtained. For this goal, we propose a focus-pixel estimation and optimization based multi-focus image fusion framework in this paper. Because the focused pixels of an image are in the same depth of field (DOF), we propose a multi-scale focus-measure algorithm for the focused pixels matting to integrate the focused region firstly. Then, the boundaries of focused and defocused regions are obtained accurately by the proposed optimizing strategy. And the boundaries are also fused to reduce the influence of insufficient boundary precision. The experimental results demonstrate that the proposed method outperforms some previous typical methods in both objective evaluations and visual perception.

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“…The region based on image fusion is put in feature-based image fusion in some scientific articles [3]. This category is supported by using probability filtering and the region correction method to generate a fused image [4]. In order to sharpen the focus region and decrease computation cost, the applying Laplacian kernel is implemented in [5].…”

Section: Introductionmentioning

confidence: 99%

Multi Focus Image Fusion with Region-Center Based Kernel

Ismail

Hawari

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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The usage of the camera is ubiquitous nowadays. It provides highly accurate information. Then, the camera helps humans to carry out their specific tasks correctly. Furthermore, the camera becomes an important tool to achieve accurate computation in some fields, such as in medical diagnostic, robotics, remote sensing, and others. On the other side, the camera also has a weakness to capture detailed information of the scene in one image. Many images are needed to obtain the focus information of all the scenes since the lens's limitation depth of field produces out of focus regions beyond the focused object. To make a settlement of that case, the researchers have found a multi-focus image fusion process. This process selects all detailed information from a sequence of images and fuses them into one focused image. Through this fused image, the user such as human and machine can read the focus information easier. Later, the researchers developed multi-focus image fusion methods which various advanced procedures and algorithms. Furthermore, the implementation of multi-focus image fusion in new fields multiplied in the last two decades ago. It was able to create an accurate and efficient method to build a fused image. The proposed method is a kind of a new method in multi-focus image fusion. It works according to the region-center based kernel. The kernel processes input image to predict the detailed information of the scene. This method is robust to prevent the unexpected effects and sensitivities of noise. The proposed method generates a fused image with high accuracy. Finally, the assessment is done based on mutual information and structure similarity.

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Multi-focus image fusion based on probability filtering and region correction

Cited by 17 publications

References 32 publications

Exploring Types of Multi-Focus Image Fusion

Exploring Types of Multi-Focus Image Fusion

Focus-pixel estimation and optimization for multi-focus image fusion

Multi Focus Image Fusion with Region-Center Based Kernel

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