Multi-scale decomposition based fusion of infrared and visible image via total variation and saliency analysis

Ma, Tao; Ma, Jie; Fang, Bin; Hu, Fangyu; Quan, Siwen; Du, Huajun

doi:10.1016/j.infrared.2018.06.002

Cited by 36 publications

(10 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jian et al proposed an image fusion method for via rolling guidance filter-based decomposition [19]; this method can better preserve valid structural information and details. Ma et al utilized a Gaussian filter to achieve the two-scale decomposition of source images and applied different fusion rules to integrate the layers, preserving the thermal radiation and details of source images [20]. Zhu et al proposed a hybrid multi-scale decomposition scheme with guided image filtering [21] and introduced three diverse fusion rules for different scales [22].…”

Section: A Decomposition-based Methodsmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

2020

View full text Add to dashboard Cite

Medical image fusion techniques primarily integrate the complementary features of different medical images to acquire a single composite image with superior quality, reducing the uncertainty of lesion analysis. However, the simultaneous extraction of more salient features and less meaningless details from medical images by using multi-scale transform methods is a challenging task. This study presents a two-scale fusion framework for multimodal medical images to overcome the aforementioned limitation. In this framework, a guided filter is used to decompose source images into the base and detail layers to roughly separate the two characteristics of source images, namely, structural information and texture details. To effectively preserve most of the structural information, the base layers are fused using the combined Laplacian pyramid and sparse representation rule, in which an image patch selection-based dictionary construction scheme is introduced to exclude the meaningless patches from the source images and enhance the sparse representation capability of the pyramid-decomposed low-frequency layer. The detail layers are subsequently merged using a guided filtering-based approach, which enhances contrast level via noise filtering as much as possible. The fused base and detail layers are reconstructed to generate the fused image. We experimentally verify the superiority of the proposed method by using two basic fusion schemes and conducting comparison experiments on nine pairs of medical images from diverse modalities. The comparison of the fused results in terms of visual effect and objective assessment demonstrates that the proposed method provides better visual effect with an improved objective measurement because it effectively preserves meaningful salient features without producing abnormal details. INDEX TERMS Medical image fusion, guided filtering, sparse representation, salient features, meaningless details.

show abstract

Section: A Decomposition-based Methodsmentioning

confidence: 99%

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

2020

View full text Add to dashboard Cite

show abstract

“…Moreover, with the goal of simultaneously preserving the appearance information of the visible image and the thermal radiation of the infrared image, Ma et al put forward a novel fusion algorithm of visible and infrared images-referred to as Gradient Transfer Fusion (GTF)-based on gradient transfer and total variation (TV) minimization [81]. In [82], a Gaussian filter is utilized to decompose multi-source images, with a new combination of total variation rules employed to combine the base layers and a novel weight map construction method being proposed based on the saliency analysis. Furthermore, by using Nonsubsampled Contourlet Transform (NSCT) and sparse K-SVD dictionary learning to obtain the prominent features of the source images, Cai et al proposed a novel fusion method which named as NSCT_SK_SVD [83].…”

Section: Combination Of Different Transformsmentioning

confidence: 99%

Multi-Sensor Image Fusion: A Survey of the State of the Art

Li¹,

Yu²,

Zhang³

et al. 2021

JCC

View full text Add to dashboard Cite

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

show abstract

“…(3.2) Select the intelligent wolf with the best objective function value as the alpha wolf, mark its position as X L and objective function value as Y L , and regard the S_num intelligent wolf with the maximum objective function value, with the exception of the alpha wolf as the scouting wolf, which will perform scouting behavior according to formulas (18) and (19) until the number of scouting times of each scouting wolf reaches the maximum scouting times T max ;…”

Section: Fusion Schemementioning

confidence: 99%

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Feng

Lou

2020

IEEE Access

View full text Add to dashboard Cite

The purpose of image fusion is to merge a substantial amount of information, such as contour, texture and intensity distribution information from original images, with a fusion image. To retain a considerable amount of the fusion image while retaining the texture details of the source image and maintaining the edge of the source image, this paper proposes an improved infrared and visible image fusion algorithm that is based on total variation. First, source infrared and visible light images and a difference image were decomposed by a total variation model, and their respective cartoon and texture components were acquired. A fitness function was solved according to the entropy, standard deviation and edge similarity of the infrared and visible light images. The optimal combination weight of various kinds of texture and cartoon components was sought via a wolf pack intelligence optimization algorithm to acquire the final fusion results with high-quality contrast ratio and edge details. The experimental results indicate that the proposed method not only can preserve edge contour information about the original image but also can effectively retain its texture detail information. The method is superior to the traditional multiscale and sparse representation fusion method with regard to various indicators, such as subjective visual effect, mutual information, gradient information, structural similarity and visual sensitivity. INDEX TERMS Infrared and visible light, image fusion, total variation model, wolf pack algorithm.

show abstract

Multi-scale decomposition based fusion of infrared and visible image via total variation and saliency analysis

Cited by 36 publications

References 48 publications

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

Two-Scale Multimodal Medical Image Fusion Based on Guided Filtering and Sparse Representation

Multi-Sensor Image Fusion: A Survey of the State of the Art

Infrared and Visible Image Fusion Based on the Total Variational Model and Adaptive Wolf Pack Algorithm

Contact Info

Product

Resources

About