Joint patch clustering-based dictionary learning for multimodal image fusion

Kim, Minjae; Han, David K.; Ko, Hanseok

doi:10.1016/j.inffus.2015.03.003

Cited by 174 publications

(108 citation statements)

References 56 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…The promising results have been shown in various image restoration applications [38,45]. Based on the classification of image patches, this paper proposed sparse representation-based approach that uses PCA algorithm to construct more informative and compact dictionary [38].…”

Section: Dictionary Learning Analysismentioning

confidence: 99%

“…The high computational complexity constrains the learned dictionary size of KSVD in practical usage. Kim [38] firstly applied clustering-based dictionary learning solution to image fusion. It clustered patches from different source images together based on local structural similarities.…”

Section: Dictionary Learning Analysismentioning

confidence: 99%

“…Wang and Liu [37] proposed an approximate K-SVD based sparse representation method for image fusion and exposure fusion to reduce computation costs. Kim and Han [38] proposed a joint clustering dictionary learning method for image fusion. They used the steering kernel regression to strength the local geometric features of source images first.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Zhu

Chai

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

Abstract:In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA) to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP) algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.

show abstract

Section: Dictionary Learning Analysismentioning

confidence: 99%

Section: Dictionary Learning Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

Zhu

Chai

et al. 2017

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…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%

“…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. And Zhao et al (2016) propose the detection of saliency features in both images using sliding windows in order to fuse them.…”

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

View full text Add to dashboard Cite

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.

show abstract

Multi‐Objective Differential Development Using DNN for Multimodality Medical Image Fusion

Ranjith Kumar,

Dondapati,

Sharma

et al. 2024

Human Cancer Diagnosis and Detection Using Exascale Computing

View full text Add to dashboard Cite

Joint patch clustering-based dictionary learning for multimodal image fusion

Cited by 174 publications

References 56 publications

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

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

Multi‐Objective Differential Development Using DNN for Multimodality Medical Image Fusion

Contact Info

Product

Resources

About