Infrared and visible image fusion via gradient transfer and total variation minimization

Ma, Jiayi; Chen, Chen; Li, Chang; Huang, Jun

doi:10.1016/j.inffus.2016.02.001

Cited by 940 publications

(400 citation statements)

References 36 publications

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“…On the other hand, works such as Ma et al (2016), Kim et al (2016) and Zhao et al (2016) follow a different approach. Despite the fact that these works are purely academic, and once the image registration process was performed, they automatically found properties in both images (infrared and visual spectrum) in order to fuse them, and generating a new composite image.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the fact that these works are purely academic, and once the image registration process was performed, they automatically found properties in both images (infrared and visual spectrum) in order to fuse them, and generating a new composite image. In Ma et al (2016) canny edges are extracted in both images and the registration is conceived as a minimization problem. In Kim et al (2016) an image patch dictionary is built using principal component analysis to combine the multimodal images.…”

Section: Related Workmentioning

confidence: 99%

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Herramienta software para inspección termográfica usando fusión multimodal de imágenes térmicas y de espectro visible

Ospina¹,

Cardona²,

Bacca-Cortes³

2017

IyC

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Monocular cameras are commonly used in communication devices, entertainment, and industrial environments since they allow people to recognize rapidly scenes and objects. On the other hand, thermal cameras are mostly known in industrial environments to visualize the objects' thermic radiance but they lose their visual details. This work presents the design and implementation of a thermography inspection tool -INVIfusion -to fuse infrared and visual spectrum images. This tool includes three modules: image acquisition and calibration module, multimodal image fusion module, and report generation module. The main contribution of this work compared with other software inspection tools is INVIfusion supports cameras from different manufacturers (infrared and visible spectrum), having different field of view and spatial resolutions. To validate the suggested system's functionality and accuracy, quantitative and qualitative tests were performed considering different camera configurations. In all cases the thermal camera was a FLIR E320. Quantitative tests were performed measuring the mean symmetric re-projection error obtaining a maximum error of 2.08 ± 1.8 pixels. Keywords: Multimodal image fusion, software inspection tool, thermography. ResumenLas cámaras monoculares son comúnmente usadas en dispositivos de comunicación, y ambientes de entretenimiento e industriales ya que ellas permiten a la gente rápidamente reconocer diferentes escenas y objetos. Por otra parte, las cámaras térmicas son ampliamente conocidas en ambientes industriales para visualizar la radiación térmica de los objetos pero estas cámaras pierden los detalles visuales. Este trabajo presenta el diseño e implementación de una herramienta de inspección Termográfica que permite fusionar imágenes térmicas y de espectro visible -INVIfusion. Esta herramienta incluye 3 módulos: el módulo de adquisición de imágenes y calibración, el módulo de fusión de imágenes, y el módulo de generación de reportes. La principal contribución de este trabajo en comparación con otros aplicativos de inspección es que INVIfusion soporta diferentes cámaras de diferentes fabricantes (espectro visible o térmico), teniendo diferentes campos de visión y resoluciones espaciales. Para validar la funcionalidad y precisión del sistema propuesto, pruebas cuantitativas y cualitativas fueron realizadas considerando diferentes configuraciones de cámaras. En todos los casos la cámara térmica fue una FLIR E320. Pruebas cuantitativas fueron realizadas midiendo el error de reproyección simétrico normalizado obteniendo un máximo error de 2.08 ± 1.8 pixeles.Palabras clave: Fusión de imágenes, herramienta software de inspección, termografía.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Herramienta software para inspección termográfica usando fusión multimodal de imágenes térmicas y de espectro visible

Ospina¹,

Cardona²,

Bacca-Cortes³

2017

IyC

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show abstract

“…Finally, the synthesized HR HF NIR image is added to the LR NIR image to generate a HR NIR image. The experimental results show that the proposed algorithm provides a higher PSNR of 0.94 dB than the state-of-the-art [7] in low light environments.…”

Section: Introductionmentioning

confidence: 97%

“…Meanwhile, various approaches for acquiring IR images and corresponding VIS images together and fusing them to generate a desired IR image have been proposed [5][6][7][8]. Recently, Ma et al proposed an IR/VIS fusion method based on gradient transfer and total variation (TV) minimization so that it can keep both the thermal radiation and the appearance information in the source images [7].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Ma et al proposed an IR/VIS fusion method based on gradient transfer and total variation (TV) minimization so that it can keep both the thermal radiation and the appearance information in the source images [7]. Bavirisetti and Dhuli utilized anisotropic diffusion to decompose the source images into approximation and detail layers, and computed final detail and approximation layers with the help of Karhunen-Loeve transform (KLT) [8].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network-based infrared image super resolution under low light environment

Han

Kim

Song

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images

Wang

Yang

2020

J Applied Clin Med Phys

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In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two-stage fusion framework for computed tomography (CT) and magnetic resonance (MR) images. First, the intensity and geometric structure features in both CT and MR images are extracted by the saliency detection method and structure tensor, respectively, and an initial fused image is obtained. Then, the initial fused image is optimized by a variational model which contains a fidelity term and a regularization term. The fidelity term is to retain the intensity of the initial fused image, and the regularization term is to constrain the gradient information of the fused image to approximate the MR image. The primal-dual algorithm is proposed to solve the variational problem. The proposed method is applied on five pairs of clinical medical CT and MR-T1\MR-T2 images, and the comparison metrics SF, MI, Q AB=F , Q W , and VIFF are calculated for assessment. Compared with seven state-of-the-art methods, the proposed method shows a comprehensive advantage in preserving the salient intensity features, as well as texture structure information, not only in visual effects but also in objective assessments.

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Infrared and visible image fusion via gradient transfer and total variation minimization

Cited by 940 publications

References 36 publications

Herramienta software para inspección termográfica usando fusión multimodal de imágenes térmicas y de espectro visible

Herramienta software para inspección termográfica usando fusión multimodal de imágenes térmicas y de espectro visible

Convolutional neural network-based infrared image super resolution under low light environment

An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images

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