Multifocus color image fusion using quaternion wavelet transform

Pang, Haochen; Zhu, Ming

doi:10.1109/cisp.2012.6469884

Cited by 30 publications

(15 citation statements)

References 14 publications

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“…In the multi-scale transform domain, there are many fusion tools, such as the discrete wavelet transform(DWT) [2], contourlet transform [53], shift-invariant shearlet transform [45], quaternion wavelet transform [35] and nonsubsampled shearlet transform [29] etc. These decomposition methods project source images to the frequency domain, which increases the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

358

149

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Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent lowrank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.

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Section: Introductionmentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

358

149

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“…His preliminary outcomes exhibited that the proposed technique proved superior to various contemporary denoising methodologies. Haochen et al [14] proposed a multi-focus color image fusion calculation with respect to QWT. His main objective was to deal with the issue of obscured images.…”

Section: Literature Surveymentioning

confidence: 99%

A Novel Bayesian Patch-Based Approach for Image Denoising

2020

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Recently patch-based image denoising techniques have gained the attention of researchers as it is being used in numerous image denoising applications. This article is proposing a new Bayesian Patch-based image denoising algorithm using Quaternion Wavelet Transform (QWT) for grayscale images. In the proposed work, a patch model has been used instead of the Gibbs distribution based energy model. Experimental results indicate that the proposed algorithm effectively diminishes noise. The results of the developed approach are also compared with other efficient image denoising algorithms such as Expected Patch Log Likelihood (EPLL), Block-matching and 3D filtering (BM3D), Patch-Based Locally Optimal Wiener (PLOW), Weighted Nuclear Norm Minimization (WNNM), Hybrid Robust Bilateral Filter-Total Variation Filter (RBF-TVF) and Hybrid Total Variation Filter-Weighted Bilateral Filter (TVF-WBF) methods. The comparison revealed that the outcomes of the given approach are much sharper, clearer, and having the highest quality in comparison with other patch-based methods. INDEX TERMS Image denoising, Bayesian patch-based method, PSNR, quaternion wavelet transform (QWT).

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“…In another work, Wang et al [7] used shearlet transform to solve the problem of bad image representations in multi‐modal medical image fusion. Aiming at addressing the blur problem in colour image fusion, Pang et al [6] proposed a multifocus colour image fusion algorithm based on the quaternion wavelet transform. Although this method can remove the blur regions of different images, it is not robust to varying degrees of blurs.…”

Section: Related Workmentioning

confidence: 99%