Infrared and visible image fusion using co-occurrence filter

Zhang, Ping; Yuan, Yuhong; Fei, Chun; Tian, Pu; Wang, Shuhang

doi:10.1016/j.infrared.2018.08.004

Cited by 35 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

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“…The techniques are ordered according to time, chronologically from the oldest to the newest algorithm. The fusion process is carried out according to techniques described in [5, 8, 9, 27, 31, 32, 34, 35, 38–43]. The dataset used is the most challenging dataset.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…So, these parameters are exploited to influence the smoothing algorithm such that the effective edges remain preserved. Mathematically, it can be defined as [38]

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ I_{m} = \frac{\sum_{n ϵ N (m)} w false(m, n false) I_{n}}{\sum_{n ϵ N (m)} w false(m, n false)} \end{matrix}

where

I_{m}

and

I_{n}

are the output and input pixel values, respectively. The Gaussian filter kernel and bilateral filter kernel are defined in Section 2.4 and if we replace the Gaussian kernel with the normalised co‐occurrence matrix [38]

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ M (i, j) = bold-italicC false(i, j false) / h ()(, i) h false(j false) \end{matrix}

where

bold-italicC false(i, j false)

is the co‐occurrence matrix,

h false(i false)

and

h false(j false)

are the frequencies of occurrence of pixels i and j , then co‐occurrence filter is obtained.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

“…A hybrid IR and visible image fusion method is proposed by Zhang et al . [38] based on the co‐occurrence filter and maximum symmetric surround (MSS) saliency extraction technique. The base and detail layers were extracted using Gaussian filter and then salient object information extracted from detail layers, using MSS was passed through co‐occurrence filter to generate optimal weight for detail layers.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

See 2 more Smart Citations

From pyramids to state‐of‐the‐art: a study and comprehensive comparison of visible–infrared image fusion techniques

Sharma

Dogra

Goyal

et al. 2020

IET Image Processing

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Section: Experiments and Resultsmentioning

confidence: 99%

“…So, these parameters are exploited to influence the smoothing algorithm such that the effective edges remain preserved. Mathematically, it can be defined as [38]

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ I_{m} = \frac{\sum_{n ϵ N (m)} w false(m, n false) I_{n}}{\sum_{n ϵ N (m)} w false(m, n false)} \end{matrix}

where

I_{m}

and

I_{n}

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ M (i, j) = bold-italicC false(i, j false) / h ()(, i) h false(j false) \end{matrix}

where

bold-italicC false(i, j false)

is the co‐occurrence matrix,

h false(i false)

and

h false(j false)

are the frequencies of occurrence of pixels i and j , then co‐occurrence filter is obtained.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Section: State‐of‐the‐artmentioning

confidence: 99%

See 1 more Smart Citation

From pyramids to state‐of‐the‐art: a study and comprehensive comparison of visible–infrared image fusion techniques

Sharma

Dogra

Goyal

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

“…e fusion methods used for experiment in this paper are as follows: guided filter algorithm (GFF) [23], two-scale using saliency detection (TSD) [37], contrast pyramid algorithm (CP) [40], convolution sparse representation (CSR) algorithm [41], gradient-based transfer and total variation algorithm (GTTV) [42], weighted least squares optimization (WLS) algorithm [22], multiple visual feature measurement-based fusion (MVFMF) approach [24], co-occurrence filter-based fusion method (CoF) [25], cross bilateral filter (CBF) [21], targetenhanced multiscale transform decomposition algorithm (TEMTD) [17], and latent low-rank representation algorithm (LLRR) [43].…”

Section: Compared Methodsmentioning

confidence: 99%

“…Bilateral filter [20], cross bilateral filter [21], iterative guided filtering [22], guided filtering (GFF) [23], gradient domain-guided filtering (GDGF) [24], and co-occurrence filter [25] (CoF) methods are the popular local filter-based methods. e visual quality of the fused images could be significantly enhanced through the mentioned filtering methods.…”

Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Fusion with Hybrid Image Filtering

Zhang

Zhu

2020

Mathematical Problems in Engineering

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Image fusion is an important technique aiming to generate a composite image from multiple images of the same scene. Infrared and visible images can provide the same scene information from different aspects, which is useful for target recognition. But the existing fusion methods cannot well preserve the thermal radiation and appearance information simultaneously. Thus, we propose an infrared and visible image fusion method by hybrid image filtering. We represent the fusion problem with a divide and conquer strategy. A Gaussian filter is used to decompose the source images into base layers and detail layers. An improved co-occurrence filter fuses the detail layers for preserving the thermal radiation of the source images. A guided filter fuses the base layers for retaining the background appearance information of the source images. Superposition of the fused base layer and fused detail layer generates the final fusion image. Subjective visual and objective quantitative evaluations comparing with other fusion algorithms demonstrate the better performance of the proposed method.

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