Research on MR-SVD based visual and infrared image fusion

Song, Yajun; Xiao, Jing; Yang, Jinbao; Chai, Zhi; Wu, Yue

doi:10.1117/12.2246341

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…The transform domain-based fusion method is the current mainstream infrared and visible light image fusion method, whose main idea is to map the source images to be fused from the spatial domain into one sparser transform domain, conduct corresponding fusion in this transform domain according to some fusion principles, and acquire the fusion results after inverse transformation. The main related methods include the Wavelet Transform (WT) [5], Curvelet Transform (CT) [6], Non-Sampled Contourlet Transform (NSCT) [7], Non-Sampled Shearlet Transform (NSST) [8], [9], Directionlet Transform [10], empirical mode decomposition [11], internal generative mechanism [12], multiresolution singular value decomposition [13], Tetrolet Transform (TT) [14], Top-hat transform [15], Sparse Representation (SR) [16] and Total Variation (TV) decomposition method [17]- [19]. The spatial domain method directly extracts useful information from fusion in the spatial domain without a decomposition or reconstruction step and includes the significance fusion method and subspace-based fusion method [8], [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

“…Zhu et al [15] presented a multiscale infrared and visible light image fusion method that is based on the improved Top-hat transform model, which highlights the object of an infrared image and preserves more detail information about a visible light image than the traditional multiscale method. Song et al [13] used multiresolution SVD to decompose the source image into smoother and more refined subimages for fusion. In recent years, deep learning has demonstrated a state-of-the-art performance in various fields, such as computer vision, pattern recognition, and image processing.…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Lou

2020

IEEE Access

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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.

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