Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning

Li, Huafeng; He, Xiaoge; Tao, Dapeng; Tang, Yuanyan; Wang, Ruxin

doi:10.1016/j.patcog.2018.02.005

Cited by 203 publications

(88 citation statements)

References 34 publications

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“…For the evaluation of fused image, a single evaluation metric cannot fully reflect the performance of fused image [20,21]. Therefore, it is necessary to use multiple metrics to do comprehensive performance analysis.…”

Section: Objective Evaluation Metricsmentioning

confidence: 99%

A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy

Huang

Wei

et al. 2019

Entropy

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In multi-modality image fusion, source image decomposition, such as multi-scale transform (MST), is a necessary step and also widely used. However, when MST is directly used to decompose source images into high-and low-frequency components, the corresponding decomposed components are not precise enough for the following infrared-visible fusion operations. This paper proposes a non-subsampled contourlet transform (NSCT) based decomposition method for image fusion, by which source images are decomposed to obtain corresponding high-and low-frequency sub-bands. Unlike MST, the obtained high-frequency sub-bands have different decomposition layers, and each layer contains different information. In order to obtain a more informative fused high-frequency component, maximum absolute value and pulse coupled neural network (PCNN) fusion rules are applied to different sub-bands of high-frequency components. Activity measures, such as phase congruency (PC), local measure of sharpness change (LSCM), and local signal strength (LSS), are designed to enhance the detailed features of fused low-frequency components. The fused high-and low-frequency components are integrated to form a fused image. The experiment results show that the fused images obtained by the proposed method achieve good performance in clarity, contrast, and image information entropy.Entropy 2019, 21, 1135 2 of 16 for infrared-visible image fusion, have been widely applied to the target recognition in different environments, such as smart city, battlefield, remote sensing, and so on [2,3].In recent years, transform domain based methods have become the mainstream in infrared-visible image fusion, which include pyramid, wavelet transform, multi-scale geometric transform, sparse representation [4,5], and so on. Pyramid, wavelet transform, and multi-scale geometric transform can be categorized as MST-based methods. MST-based methods have three main steps. First, MST is employed to decompose each source image into high-frequency sub-bands at different scales and directions as well as one low-frequency sub-band. Then, the obtained high-and low-frequency sub-bands are fused separately following different fusion rules. Finally, the fused image is obtained by performing the inverse MST on both fused high-and low-frequency sub-bands. Double-tree complex wavelet transform as a kind of wavelet transform can only capture a limited amount of edge information, but cannot correctly and effectively represent the discontinuity of lines and curves [6]. As a true two-dimensional (2D) multi-scale geometric analysis method, contourlet transform (CT) possesses localization, multi-resolution, multi-scale, multi-direction, and anisotropy.As a shift-invariant version of CT, NSCT performs well in transform domain, and has been widely used in image fusion. NSCT has multi-scale and multi-direction features, which can solve the limitations of traditional wavelet methods in the representation of image curves and edges [7]. Compared with traditional MST-based image fusion methods, NSCT has shif...

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Section: Objective Evaluation Metricsmentioning

confidence: 99%

A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy

Huang

Wei

et al. 2019

Entropy

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“…In the literature, many methods for performing image fusion are studied [1] - [3]. This work uses the Laplacian pyramid (LP) and transforms it into discrete wavelets (DWT).…”

Section: Introductionmentioning

confidence: 99%

Wavelet Decomposition and Alpha Stable Fusion

Sabre¹,

Wahyuni²

2020

SIPIJ

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This article gives a new method of fusing multifocal images combining the Laplacian pyramid and the wavelet decomposition using the stable distance alpha as a selection rule. We start by decomposing multifocal images into several pyramid levels, then applying the wavelet decomposition to each level. the originality of this work is to use the stable distance alpha to fuse the wavelet images at each level of the Pyramid. To obtain the final fused image, we reconstructed the combined image at each level of the pyramid. We compare our method to other existing methods in the literature and we deduce that it is almost better.

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“…Image registration has a vital role in various applications such as video registration, computer vision, astrophotography, and medical image registration as well as brain image registration, and it is also the prerequisite process of image fusion . In the medical image registration, the same anatomical point on the human body has the same spatial position on different images.…”

Section: Introductionmentioning

confidence: 99%

Cross‐cumulative residual entropy‐based medical image registration via hybrid differential search algorithm

Gui

Ling

Zhang

et al. 2019

Int J Imaging Syst Tech

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Image registration is the process of overlaying images of the same scene taken at different times by different sensors from different viewpoints. The cross‐cumulative residual entropy (CCRE)‐based medical image registration could achieve a high precision and a strong robustness performance. However, the optimization problem formulated by CCRE consists of some local extrema, especially for noise images. In order to address these difficulties, this article proposes a new optimization algorithm named hybrid differential search algorithm (HDSA) to optimize CCRE. As HDSA consists of simple control parameters, it is independent of the initial searching point. In addition, HDSA ameliorated the search method and the iterative conditions. As a result, the optimization process is more stable and efficient. Image registration experiments of HDSA are performed and compared with the conventional differential search algorithm (DSA) and adaptive differential evolution with optional external archive (JADE). The results show that HDSA does not only overcome the difficulties of sticking in the local extrema but also enhances the precision of registration. It is effective, robust, and fast for both the single‐mode rigid medical image registration and the multispectral‐mode rigid medical image registration.

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Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning

Cited by 203 publications

References 34 publications

A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy

A Novel Infrared and Visible Image Information Fusion Method Based on Phase Congruency and Image Entropy

Wavelet Decomposition and Alpha Stable Fusion

Cross‐cumulative residual entropy‐based medical image registration via hybrid differential search algorithm

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