Hybrid Image Fusion Algorithm Using Laplacian Pyramid and PCA Method

Verma, Shraddha; Kaur, Manpreet; Kumar, Rohit

doi:10.1145/2905055.2905275

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…In this paper, as the number of pages is limited, we present only 3 sets of multi focus images. Figures 4, 5 For comparison purposes, we perform fusion using methods: PCA method [1], Discrete Wavelet Transform (DWT) method [6], Laplacian Pyramid LP_PCA [13] , LP_DWT [14] and Bilateral gradient (BG) [2].…”

Section: Resultsmentioning

confidence: 99%

Dempster-Shafer and Alpha Stable Distance for Multi-Focus Image Fusion

Sabre¹,

Wahyuni²

2022

Artificial Intelligence and Applications

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The aim of multi-focus image fusion is to integrate images with different objects in focus so that obtained a single image with all objects in focus. In this paper, we present a novel multi-focus image fusion method based using Dempster-Shafer Theory and alpha stable distance. This method takes into consideration the information in the surrounding region of pixels. Indeed, at each pixel, the method exploits the local variability that is calculated from quadratic difference between the value of pixel I(x,y) and the value of all pixels that belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes in DempsterShafer Theory are considered: blurred part and focus part. We show that our method give the significant result.

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Section: Resultsmentioning

confidence: 99%

Dempster-Shafer and Alpha Stable Distance for Multi-Focus Image Fusion

Sabre¹,

Wahyuni²

2022

Artificial Intelligence and Applications

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“…Visually, the image obtained by the proposed method gives a very satisfactory fusion. To compare the proposed method to other methods, we perform the fusion using methods: PCA method [1], discrete wavelet transform (DWT) method [6], Laplacian pyramid LP_PCA [13],…”

Section: Resultsmentioning

confidence: 99%

Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

Wahyuni¹,

Sabre²

2021

Computer Science and Information Technology Trends

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In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show that our method gives the significant and better result by comparing it to other methods.

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“…For comparison purposes, we performed fusion using methods: PCA method [1], Discrete Wavelet Transform (DWT) method [6], Laplacian Pyramid LP_PCA [15], LP_DWT [17] and Bilateral gradient (BG) [2].…”

Section: Resultsmentioning

confidence: 99%

Fusion of Multi-Focus Images with Neighbour Local Distance

Wahyuni¹,

Sabre²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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The aim of multi-focus image fusion is to integrate images with different objects in focus so that we obtained a single image with all objects in focus. In this paper, we present a novel multifocus image fusion method based on neighbour local variability (NLV). This method takes into consideration the information in the surrounding region of pixels. Indeed, at each pixel, the method exploits the local variability calculated from quadratic difference between the value of pixel and the value of all pixels that belong to its neighbourhood. It expresses the behaviour of pixel relative to all pixels belonging to its neighbourhood. The variability preserves edge feature because it detects the abrupt image intensity. The fusion of each pixel is performed by weighting each pixel by the exponential of the local variability. The precision of this fusion depends on the largenumberof the neighbourhood where the largenumber depends on the blurring characterized by the variance and its size of blurring filter. We constructed a model that gives the value of the large….. from the variance and the size of blurring filter. Comparing our method with other methods, it shows the best result.

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Hybrid Image Fusion Algorithm Using Laplacian Pyramid and PCA Method

Cited by 9 publications

References 8 publications

Dempster-Shafer and Alpha Stable Distance for Multi-Focus Image Fusion

Dempster-Shafer and Alpha Stable Distance for Multi-Focus Image Fusion

Dempster-Shafer and Multi-Focus Image Fusion using Local Distance

Fusion of Multi-Focus Images with Neighbour Local Distance

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