scholarly journals Exploiting Superpixels for Multi-Focus Image Fusion

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 247
Author(s):  
Areeba Ilyas ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Image Fusion ◽  
Fusion Rule ◽  
Research Area ◽  
Statistical Properties ◽  
Fusion Algorithm ◽  
Spatial Consistency ◽  
Fused Image ◽  
Focus Image ◽  
Important Research Area ◽  
Initial Focus

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

2013 ◽  
Vol 401-403 ◽  
pp. 1381-1384 ◽  
Author(s):  
Zi Juan Luo ◽  
Shuai Ding
Keyword(s):  
Image Fusion ◽  
Multiple Scales ◽  
Weighted Average ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Limited Depth

t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

scholarly journals Fusion Algorithm of Multi-focus Images with Weighted Ratios and Weighted Gradient Based on Wavelet Transform

2019 ◽  
Vol 28 (4) ◽  
pp. 505-516
Author(s):  
Wei-bin Chen ◽  
Mingxiao Hu ◽  
Lai Zhou ◽  
Hongbin Gu ◽  
Xin Zhang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Low Frequency ◽  
Fusion Rule ◽  
Fusion Algorithm ◽  
Clear Image ◽  
Fused Image ◽  
Gradient Based ◽  
Focus Image ◽  
Imaging Conditions

Abstract Multi-focus image fusion means fusing a completely clear image with a set of images of the same scene and under the same imaging conditions with different focus points. In order to get a clear image that contains all relevant objects in an area, the multi-focus image fusion algorithm is proposed based on wavelet transform. Firstly, the multi-focus images were decomposed by wavelet transform. Secondly, the wavelet coefficients of the approximant and detail sub-images are fused respectively based on the fusion rule. Finally, the fused image was obtained by using the inverse wavelet transform. Among them, for the low-frequency and high-frequency coefficients, we present a fusion rule based on the weighted ratios and the weighted gradient with the improved edge detection operator. The experimental results illustrate that the proposed algorithm is effective for retaining the detailed images.

scholarly journals Light field camera all-in-focus image acquisition based on angular information

2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Yingchun Wu, , , , ◽  
Xing Cheng ◽  
Jie Liang ◽  
Anhong Wang ◽  
Xianling Zhao
Keyword(s):  
Image Fusion ◽  
Field Data ◽  
Light Field ◽  
Spatial Information ◽  
Human Perception ◽  
Energy Difference ◽  
Image Feature ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image

Traditional light field all-in-focus image fusion algorithms are based on the digital refocusing technique. Multi-focused images converted from one single light field image are used to calculate the all-in-focus image and the light field spatial information is used to accomplish the sharpness evaluation. Analyzing the 4D light field from another perspective, an all-in-focus image fusion algorithm based on angular information is presented in this paper. In the proposed method, the 4D light field data are fused directly and a macro-pixel energy difference function based on angular information is established to accomplish the sharpness evaluation. Then the fused 4D data is guided by the dimension increased central sub-aperture image to obtain the refined 4D data. Finally, the all-in-focus image is calculated by integrating the refined 4D light field data. Experimental results show that the fused images calculated by the proposed method have higher visual quality. Quantitative evaluation results also demonstrate the performance of the proposed algorithm. With the light field angular information, the image feature-based index and human perception inspired index of the fused image are improved.

scholarly journals Multiscale Image Matting Based Multi-Focus Image Fusion Technique

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 472 ◽  
Author(s):  
Sarmad Maqsood ◽  
Umer Javed ◽  
Muhammad Mohsin Riaz ◽  
Muhammad Muzammil ◽  
Fazal Muhammad ◽  
...  
Keyword(s):  
Image Fusion ◽  
Fusion Rule ◽  
Multiple Source ◽  
Superior Performance ◽  
Spatial Gradient ◽  
Image Matting ◽  
Edge Information ◽  
Fused Image ◽  
Focus Image ◽  
Edge Detection Method

Multi-focus image fusion is a very essential method of obtaining an all focus image from multiple source images. The fused image eliminates the out of focus regions, and the resultant image contains sharp and focused regions. A novel multiscale image fusion system based on contrast enhancement, spatial gradient information and multiscale image matting is proposed to extract the focused region information from multiple source images. In the proposed image fusion approach, the multi-focus source images are firstly refined over an image enhancement algorithm so that the intensity distribution is enhanced for superior visualization. The edge detection method based on a spatial gradient is employed for obtaining the edge information from the contrast stretched images. This improved edge information is further utilized by a multiscale window technique to produce local and global activity maps. Furthermore, a trimap and decision maps are obtained based upon the information provided by these near and far focus activity maps. Finally, the fused image is achieved by using an enhanced decision maps and fusion rule. The proposed multiscale image matting (MSIM) makes full use of the spatial consistency and the correlation among source images and, therefore, obtains superior performance at object boundaries compared to region-based methods. The achievement of the proposed method is compared with some of the latest techniques by performing qualitative and quantitative evaluation.

scholarly journals Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

2019 ◽  
Vol 9 (1) ◽  
pp. 344-350 ◽  
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Performance Metrics ◽  
Depth Of Field ◽  
Stationary Wavelet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Local Mean ◽  
Non Local

Today’s research era, image fusion is a actual step by step procedure to develop the visualization of any image. It integrates the essential features of more than a couple of images into a individual fused image without taking any artifacts. Multifocus image fusion has a vital key factor in fusion process where it aims to increase the depth of field using extracting focused part from different multiple focused images. In this paper multi-focus image fusion algorithm is proposed where non local mean technique is used in stationary wavelet transform (SWT) to get the sharp and smooth image. Non-local mean function analyses the pixels belonging to the blurring part and improves the image quality. The proposed work is compared with some existing methods. The results are analyzed visually as well as using performance metrics.

An Adaptive Region Fusion Rule Multi-Focus Image Fusion Algorithm

2017 ◽  
Vol 54 (10) ◽  
pp. 101005
Author(s):  
程德强 Cheng Deqiang ◽  
陈 刚 Chen Gang ◽  
高凌志 Gao Lingzhi ◽  
厉 航 Li Hang ◽  
黄晓丽 Huang Xiaoli ◽  
...  
Keyword(s):  
Image Fusion ◽  
Fusion Rule ◽  
Fusion Algorithm ◽  
Focus Image

scholarly journals CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 904
Author(s):  
Shah Rukh Muzammil ◽  
Sarmad Maqsood ◽  
Shahab Haider ◽  
Robertas Damaševičius
Keyword(s):  
Image Fusion ◽  
Clinical Diagnosis ◽  
Fusion Rule ◽  
Magnetic Resonance Images ◽  
Superior Performance ◽  
Spatial Gradient ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Tremendous Importance ◽  
Contrast Stretching

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

An Image Fusion Method Based on Shearlet and Multi-Decision

2014 ◽  
Vol 530-531 ◽  
pp. 390-393
Author(s):  
Yong Wang
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Image Decomposition ◽  
Fusion Algorithm ◽  
Shearlet Transform ◽  
Fused Image ◽  
Focus Image ◽  
Image Detail ◽  
Image Fusion Method ◽  
Traditional Image

Image processing is the basis of computer vision. Aiming at some problems existed in the traditional image fusion algorithm, a novel algorithm based on shearlet and multi-decision is proposed. At first we discussed multi-focus image fusion and then we use Shearlet transform and multi-decision for image decomposition high-frequency coefficients. Finally, the fused image is obtained through inverse Shearlet transform. Experimental results show that comparing with traditional image fusion algorithms, the proposed approach retains image detail and more clarity.

scholarly journals Multi-Focus Fused Image using Inception-Resnet V2

2019 ◽  
Vol 8 (10) ◽  
pp. 1095-1099
Keyword(s):  
Image Fusion ◽  
High Frequency ◽  
Fusion Rule ◽  
Activity Level ◽  
Fusion Image ◽  
Fused Image ◽  
Level Information ◽  
Focus Image ◽  
3D Scene ◽  
Local Filters

Multi-focus image fusion is the process of integration of pictures of the equivalent view and having various targets into one image. The direct capturing of a 3D scene image is challenging, many multi-focus image fusion techniques are involved in generating it from some images focusing at diverse depths. The two important factors for image fusion is activity level information and fusion rule. The necessity of designing local filters for extracting high-frequency details the activity level information is being implemented, and then by using various elaborated designed rules we consider clarity information of different source images which can obtain a clarity/focus map. However, earlier fusion algorithms will excerpt high-frequency facts by considering neighboring filters and by adopting various fusion conventions to achieve the fused image. However, the performance of the prevailing techniques is hardly adequate. Convolutional neural networks have recently used to solve the problem of multi-focus image fusion. By considering the deep neural network a two-stage boundary aware is proposed to address the issue in this paper. They are: (1) for extracting the entire defocus info of the two basis images deep network is suggested. (2) To handle the patches information extreme away from and close to the focused/defocused boundary, we use Inception ResNet v2. The results illustrate that the approach specified in this paper will result in an agreeable fusion image, which is superior to some of the advanced fusion algorithms in comparison with both the graphical and objective evaluations.

scholarly journals Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

2021 ◽  
pp. 3228-3236
Author(s):  
Nada Jasim Habeeb
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Wiener Filter ◽  
Fusion Rule ◽  
Depth Of Field ◽  
Complex Wavelet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Fusion Operation ◽  
Complex Wavelet

Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The results showed that the proposed method is better than these fusion methods in terms of the focus and quality. 

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