An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement

Zhi, Qu; Xing, Yaqiong; Song, Yafei

doi:10.3390/info9120308

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…In the first group of simulation tests, we used the presented method to fuse five typical infrared and visible images in the TNO datasets, namely, "Men in front of house," "Bunker," "Sandpath," "Kaptein_1123," and "barbed_wire_2". In addition, six MGAbased methods are selected for comparison experiments, including WT [23], TEMST [35], NSST with weighted average [36], NSST with WT [37], NSCT with WT [38], and CURV with WT [39].…”

Section: Comparison With Mga-based Methodsmentioning

confidence: 99%

Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition

Xing

Cheng

Luo

et al. 2020

J Wireless Com Network

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In multi-scale geometric analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and non-subsampled shearlet transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low-and high-frequency sub-bands. Then, the wavelet transform (WT) is used to decompose high-frequency sub-bands to obtain approximate sub-bands and directional detail sub-bands. The "average" fusion rule is performed for fusion for approximate sub-bands. And the "max-absolute" fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the highfrequency sub-bands. To highlight the thermal radiation target, we construct a nonlinear transform function to determine the fusion weight of low-frequency subbands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

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Section: Comparison With Mga-based Methodsmentioning

confidence: 99%

Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition

Xing

Cheng

Luo

et al. 2020

J Wireless Com Network

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show abstract

“…MGA-based methods are selected for comparison experiments, including WT [24] , TEMST [36] , NSST with weighted average [37] , NSST with WT [38] , NSCT with WT [39] and CURV with WT [40] .…”

Section: Comparison With Mga-based Methodsmentioning

confidence: 99%

Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

Xing

LIU

Luo

et al. 2020

Preprint

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In Multi-scale Geometric Analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of the images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and Non-Subsampled Shearlet Transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the Wavelet Transform (WT) is used to decompose high-frequency sub-bands into obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

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“…In Table 1, default parameters were used during capture and other steps like a transmission from the camera to the base station, and so on. A good number of scientists and researchers proposed different kinds of approaches (e.g., NSST, guided image filter, and structural-proper kernel histograms) [23]. Several researchers like Singh and Kaur have several methodologies for automatic road extraction [24].…”

Section: Histogram Equalization Framework Designmentioning

confidence: 99%

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

Ullah

Sun

Elahi

et al. 2022

Mathematical Problems in Engineering

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The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research.

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An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement

Cited by 8 publications

References 16 publications

Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition

Infrared and visible image fusion based on nonlinear enhancement and NSST decomposition

Infrared and Visible Image Fusion Based on Nonlinear Enhancement and NSST Decomposition

Experimental and Numerical Research of Paved Microcrack Using Histogram Equalization for Detection and Segmentation

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