Infrared and multi-type images fusion algorithm based on contrast pyramid transform

Xu, Huilong; Wang, Yan; Wu, Yujing; Qian, Yunsheng

doi:10.1016/j.infrared.2016.07.016

Cited by 46 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another pyramid fusion method is Gradient pyramid technique. Performance of Gradient pyramid in achieved by applying the 4 different filters such as horizontal, vertical and two diagonal filters [15] . Now in this section we want to explain briefly the Hilbert transform (HT) and the IHS method that form our proposed method.…”

Section: Methodsmentioning

confidence: 99%

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method

2017

View full text Add to dashboard Cite

BackgroundThe process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time.MethodsWe used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (Dk) as an assessing spectral features and Average Gradient (AGk) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method.ResultsIn this paper we used three common evaluation metrics like Average Gradient (AGk) and the lowest Discrepancy (Dk) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method.ConclusionsSince that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AGk), Discrepancy (Dk) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images.

show abstract

Section: Methodsmentioning

confidence: 99%

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method

2017

View full text Add to dashboard Cite

show abstract

“…The most popular transform domain based methods are based on the multi-scale transforms. The commonly used multi-scale transforms include various pyramids [14], discrete wavelet transform (DWT) [15], contourlet [16], etc. Recently, sparse representation (SR) and its variants [17][18][19][20] have produced state-of-the-art results in multi-sensor image fusion, which exploit the self-similarity properties of natural images.…”

Section: Sar and Optical Image Fusionmentioning

confidence: 99%

Multitask Saliency Detection model for SAR Image and Its Application in SAR and Optical Image Fusion

C¹,

Qi²,

Ding³

2017

Preprint

View full text Add to dashboard Cite

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

show abstract

“…In this study, we have developed a large-DOF FFOA method to expand the DOF using a contrast pyramid fusion algorithm (CPFA) [16,17]. Based on our previous research, an absorption intensity fluctuation modulation (AIFM) effect [11] is utilized to obtain FFOA images with different focus positions.…”

Section: Introductionmentioning

confidence: 99%

Large‐depth‐of‐field full‐field optical angiography

Wang

Huang

et al. 2019

Journal of Biophotonics

View full text Add to dashboard Cite

A large‐depth‐of‐field full‐field optical angiography (LD‐FFOA) method is developed to expand the depth‐of‐field (DOF) using a contrast pyramid fusion algorithm (CPFA). The absorption intensity fluctuation modulation effect is utilized to obtain full‐field optical angiography (FFOA) images at different focus positions. The CPFA is used to process these FFOA images with different focuses. By selecting high‐contrast areas, the CPFA can highlight the characteristics and details of blood vessels to obtain LD‐FFOA images. In the optimal case of the proposed method, the DOF for FFOA is more than tripled using 10 differently focused FFOA images. Both the phantom and animal experimental results show that the LD‐FFOA resolves FFOA defocusing issues induced by surface and thickness inhomogeneities in biological samples. The proposed method can be potentially applied to practical biological experiments.

show abstract

Infrared and multi-type images fusion algorithm based on contrast pyramid transform

Cited by 46 publications

References 13 publications

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method

Multitask Saliency Detection model for SAR Image and Its Application in SAR and Optical Image Fusion

Large‐depth‐of‐field full‐field optical angiography

Contact Info

Product

Resources

About