Multi-scale image fusion through rolling guidance filter

Jian, Lihua; Yang, Xiaobo; Zhou, Zhili; Zhou, Kai; Li, Kai

doi:10.1016/j.future.2018.01.039

Cited by 65 publications

(30 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we have used MATLAB tool for our simulation and the images have been taken from the website http://imagefusion.org and paper [5].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Image fusion is an interdisciplinary area of research and has received a lot of interest in academic, industrial, hospitals, manufacturing, robotics, military and computer vision [4], [5]. The spatial domain techniques such as PCA, averaging method have low spectral resolution whereas spectral domain such as DWT, Curve-let, SWT techniques have low spatial resolution therefore it degrades the quality of output image [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Li¹,

Bhutto²,

Du³

et al. 2018

ijacsa

View full text Add to dashboard Cite

Abstract-Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg's uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.

show abstract

“…In this paper, we have used MATLAB tool for our simulation and the images have been taken from the website http://imagefusion.org and paper [5].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Li¹,

Bhutto²,

Du³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…For the spatial domain-based methods, the weighted average (WA)-based [18] was the most uncomplicated method and utilized directly weighted average of the gray value of pixels about source images. Hereafter, the guided filter (GF)-based methods [19] and the rolling guidance filter (RGF)-based methods [20] were proposed. Since spatial domain-based methods mainly adopt patches as the fuse target [21], patches with different sizes will get different results.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-focus image fusion method for improving imaging systems by using cascade-forest model

Yang

et al. 2020

J Image Video Proc.

Self Cite

View full text Add to dashboard Cite

Image fusion technology combines information from different source images of the same target and performs extremely effective information complementation, which is widely used for the transportation field, medicine field, and surveillance field. Specifically, due to the limitation of depth of field in imaging device, images cannot focus on all objects and miss partial details. To deal with this problem, an effective multi-focus image fusion method is proposed in this paper. We interpret the production of the focus map as a two-class classification task and solve this problem by using a method based on the cascade-forest model. Firstly, we extract the specific features from overlapping patches to represent the clarity level of source images. To obtain the focus map, feature vectors are fed into the pre-trained cascade-forest model. Then, we utilize consistency check to acquire the initial decision map. Afterward, guided image filtering is used for edge-reservation to refine the decision map. Finally, the result is obtained through pixel-wise weighted average strategy. Extensive experiments demonstrate that the proposed method achieves outstanding visual performance and excellent objective indicators.

show abstract

“…Chen et al proposed an infrared-visible image fusion method combining RGF and multi-directional decomposition [ 17 ]. Jian et al combined RGF and JBF together for image fusion [ 18 ]. Although these methods achieve quite good performance on many types of images, they still have the following disadvantages: (1) the redundant information between source images leads to low information entropy of fused images; (2) contrast of fused images is likely to decrease.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan

Yang

Zhang

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ( M I )—5.3377, feature mutual information ( F M I )—0.5600, normalized weighted edge preservation value ( Q A B / F )—0.6978 and nonlinear correlation information entropy ( N C I E )—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

show abstract

Multi-scale image fusion through rolling guidance filter

Cited by 65 publications

References 37 publications

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

A novel multi-focus image fusion method for improving imaging systems by using cascade-forest model

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Contact Info

Product

Resources

About