Image fusion models and techniques at pixel level

Salem, Yosra Ben; Hamrouni, Kamel; Solaiman, Basel

doi:10.1109/ipas.2016.7880115

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…On the other hand, if we compared the proposed method with the state-of-art image fusion techniques, the basic spatial domain and frequency fusion methods are less complex but all have the problems of artifacts, blurring, long running time, enhancing the signal-to-noise ratio, and color distortion. 1,2,18 The latest techniques based on ML models and DL have better performances. In these methods, the workload is huge and the cost is expensive, which tends to address the lack of data with over-fitting.…”

Section: Details Of Experiments Done On 38 Brain Tumor Patientsmentioning

confidence: 99%

“…These are categorized into (a) spatial domain techniques and (b) transfer domain techniques. 18 A few spatial domain methods are the averaging method, select maximum/minimum method, Brovey transform method, intensity hue saturation method, and principal component analysis method. The spatial domain method directly deals with the pixel values present in images.…”

Section: Introductionmentioning

confidence: 99%

“…As far as the process domain of fusion is concerned, researchers have developed different techniques to fulfill image fusion tasks. These are categorized into (a) spatial domain techniques and (b) transfer domain techniques 18 . A few spatial domain methods are the averaging method, select maximum/minimum method, Brovey transform method, intensity hue saturation method, and principal component analysis method.…”

Section: Introductionmentioning

confidence: 99%

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Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Saini

Mathur

2022

J. Med. Imag.

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Purpose:The technique of fusing or integrating medical images collected from single and various modalities is known as medical image fusion. This is done to improve the quality of the images and combine information from several medical images. The whole procedure aids medical practitioners in gaining correct information from single images. Image fusion is one of the fastest-growing research topics in the medical imaging field. Sparse modeling is a popular signal representation technique used for image fusion with different dictionary learning approaches. We propose a medical image fusion with sparse representation (SR) and block total least-square (BLOTLESS) update dictionary learning.Approach: The domain of dictionary learning is the most significant research domain related to SR. An efficient dictionary increases the effectiveness of sparse modeling. Due to SR being an ongoing interesting research area, the medical image fusion process is done with a modified image fusion framework with recently developed BLOTLESS update dictionary learning. Results:The experimental results are compared for the image fusion process using other stateof-the-art dictionary learning algorithms, such as simultaneous codeword optimization, method of optimal directions, and K-singular value decomposition. The effectiveness of the algorithm is evaluated based on image fusion quantitative parameters. Results show that the BLOTLESS update dictionary algorithm is a promising modification for the sparse-based image fusion with its applicability in the fusion of images related to different diseases. Conclusions:The experiments and results show that the dictionary learning algorithm plays an important role in the sparse-based image fusion general framework. The fusion results also show that the proposed improved image fusion framework for medical images is promising compared with frameworks with other dictionary learning algorithms. As an application, it is also used as a tool for the fusion of different modularities of images related to brain tumor and glioma.

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Section: Details Of Experiments Done On 38 Brain Tumor Patientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Saini

Mathur

2022

J. Med. Imag.

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“…Image fusion is performed by spatial domain fusion method. We particularly used pixel level fusion using the maximum method from spatial domain [42]. The maximum method performs a selection process.…”

Section: B Segmentationmentioning

confidence: 99%

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

Aleem

Sheng

et al. 2019

IEEE Trans. Ind. Inf.

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The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.

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“…Mirajkar Pradnya P, Ruikar Sachin D (2013) [10] explained four different methods based on wavelet transform and wavelet based image fusion and concluded hybrid architectures provides better results in all test cases and also concluded SWT based image fusion method at level 2 is outperforms than all other methods. Yosra BEN SALEM and all (2016) [11] presented normal view about fundamental models and algorithms used in the image fusion, explains different strategies which guaranties the selection of similar data and decreasing theredundancy. Aishwarya et all(2016) [12]demonstrated a novel multi-focusfusion method utilizing DWT and sparse representations and verified the propose method preserve focused regions of input images with decreased artifacts.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Image Fusion Requirements, Techniques, Evaluation Metrics, and Its Applications

Narsaiah¹,

Vathsal²,

Reddy³

2018

IJET

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Fusion refers to combining two or more distinct things, the main objective of employing fusion is to generate results that provides the most detailed, reliable and accurate information possible. The image fusion is one of the main branchof data fusion. In image fusion the images are fused at different levels of images like pixel, feature and decision level. The necessity of image fusion for high resolution on multispectral and panchromatic images or realtime images for better vision. This paper reviews the general requirements of image fusion, widely used image fusion techniques such as PCA,IHS,DWT,NSCT etc.,summarizes the Quality Assessment Metrics in terms of metric, description and its principle, finally image fusion applications in various fields such as object detection, object identification, optimization and pattern reorganization, medical imaging, etc.,

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Image fusion models and techniques at pixel level

Cited by 6 publications

References 7 publications

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Fast and Accurate Retinal Identification System: Using Retinal Blood Vasculature Landmarks

A Survey on Image Fusion Requirements, Techniques, Evaluation Metrics, and Its Applications

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