Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter

Hayat, Naila; Imran, Muhammad

doi:10.1016/j.jvcir.2019.06.002

Cited by 85 publications

(86 citation statements)

References 32 publications

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“…To verify the effectiveness in multi-exposure image fusion, we compare it with three existing methods: DSIFT (Hayat and Imran 2019), EF (Mertens, Kautz, and Van Reeth 2007) and AWPIGG (Lee, Park, and Cho 2018).…”

Section: Results On Multi-exposure Image Fusionmentioning

confidence: 99%

Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

Zhang

Xiao

et al. 2020

AAAI

340

142

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In this paper, we propose a fast unified image fusion network based on proportional maintenance of gradient and intensity (PMGI), which can end-to-end realize a variety of image fusion tasks, including infrared and visible image fusion, multi-exposure image fusion, medical image fusion, multi-focus image fusion and pan-sharpening. We unify the image fusion problem into the texture and intensity proportional maintenance problem of the source images. On the one hand, the network is divided into gradient path and intensity path for information extraction. We perform feature reuse in the same path to avoid loss of information due to convolution. At the same time, we introduce the pathwise transfer block to exchange information between different paths, which can not only pre-fuse the gradient information and intensity information, but also enhance the information to be processed later. On the other hand, we define a uniform form of loss function based on these two kinds of information, which can adapt to different fusion tasks. Experiments on publicly available datasets demonstrate the superiority of our PMGI over the state-of-the-art in terms of both visual effect and quantitative metric in a variety of fusion tasks. In addition, our method is faster compared with the state-of-the-art.

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Section: Results On Multi-exposure Image Fusionmentioning

confidence: 99%

Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

Zhang

Xiao

et al. 2020

AAAI

340

142

View full text Add to dashboard Cite

show abstract

“…Lastly image integration was achieved with pyramid decomposition technique. Investigational outcomes showed the advantage of the suggested more over some previous advanced techniques [14]. Martorell et al, (2019) projected a new procedure for MEF.…”

Section: Literature Reviewmentioning

confidence: 92%

An Advanced Multi-Exposure Image Fusion Technique To Enhance The QoS for HDR Images

2020

IJATCSE

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Image fusion is carried out on images taken at different exposures levels to get well exposed image that contains details related to the entire dynamic range (DR). Unwanted objects can emerge or disappear from the image while capturing the image at different exposure levels, thus results in ghost artifacts in the fused image. Removal of such ghosting artifacts at less computational cost to produce high dynamic range image (HDRI) is of significant consideration. In this work, data base is made which contains source input images having various presentation levels (≥ 3) along with combined pictures created by both traditional and best in class image fusion algorithms, then we have suggested an advanced multi exposure image fusion (AMEF) technique to improve the QoS for HDR Images. The main objective of the study is to produce the ghost-free HDR image from multiple images taken at different exposure levels. A moving object removal method (MORM) is used to remove the ghosting relics in the fused image. Finally, we need to compare the achieved results of the proposed technique with the existing techniques, the software considered for the implementation is MATLAB.

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“…The discretion of SIST mainly consists of two steps [ 21 ]: the multi-scale partition and the directional localization. To provide the shift-invariance, the former step is done by the non-sub-sampled pyramid filters, and the latter step is implemented by using the filters of shearing.…”

Section: Methodsmentioning

confidence: 99%

“…The SIFT feature is based on the local points of interest on the object and is independent of the size and rotation of the image. So, its tolerance for changes in noise and micro-viewpoints is quite high [ 21 ]. From this point of view, it is more suitable than the structure features for medical image fusion.…”

Section: Introductionmentioning

confidence: 99%

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

Wang

Chang

Liu

et al. 2021

Journal of Healthcare Engineering

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The traditional medical image fusion methods, such as the famous multi-scale decomposition-based methods, usually suffer from the bad sparse representations of the salient features and the low ability of the fusion rules to transfer the captured feature information. In order to deal with this problem, a medical image fusion method based on the scale invariant feature transformation (SIFT) descriptor and the deep convolutional neural network (CNN) in the shift-invariant shearlet transform (SIST) domain is proposed. Firstly, the images to be fused are decomposed into the high-pass and the low-pass coefficients. Then, the fusion of the high-pass components is implemented under the rule based on the pre-trained CNN model, which mainly consists of four steps: feature detection, initial segmentation, consistency verification, and the final fusion; the fusion of the low-pass subbands is based on the matching degree computed by the SIFT descriptor to capture the features of the low frequency components. Finally, the fusion results are obtained by inversion of the SIST. Taking the typical standard deviation, QAB/F, entropy, and mutual information as the objective measurements, the experimental results demonstrate that the detailed information without artifacts and distortions can be well preserved by the proposed method, and better quantitative performance can be also obtained.

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Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter

Cited by 85 publications

References 32 publications

Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

Rethinking the Image Fusion: A Fast Unified Image Fusion Network based on Proportional Maintenance of Gradient and Intensity

An Advanced Multi-Exposure Image Fusion Technique To Enhance The QoS for HDR Images

A Medical Image Fusion Method Based on SIFT and Deep Convolutional Neural Network in the SIST Domain

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