Image Fusion Based on Multi-wavelet Transform

Tang, Guoliang; Pu, Jiexin; Huang, Xinhan

doi:10.1109/icma.2006.257590

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…The results are listed in table 1. Fig.3-4 and Table 1, we can obtain the conclusion that the image fusion algorithm we presented in this paper not only achieves better subjective visual effect, but also significantly improves the objective performance parameters such as clarity and spatial frequency, compared with the single wavelet algorithm [5] and ordinary multi-wavelet algorithm [6].…”

Section: Experimenta Resultsmentioning

confidence: 78%

“…Fig.3(c) is the fusion image with scalar wavelet (bior5.5) fusion algorithm based on local energy [5]. Fig.3(d) is the fusion image with multi-wavelet (using orthogonal multi-wavelet sa4) fusion algorithm whose hign-frequency operator is based on regional gradient and low-frequency is based on weighted average value [6]. Fig.…”

Section: Experimenta Resultsmentioning

confidence: 98%

“…Lastly, after window-based consistency verification, fusion image is obtained from the reconstructed multi-wavelet coefficients by applying inversed multi-wavelet transform. Compared to the single wavelet fusion algorithm based on local energy [5] and the multi-wavelet fusion algorithm whose high-frequency operator is based on regional gradient and its low-frequency is based on weighted average value [6], the new algorithm proposed here can eliminate the edge effect in fusion image, enhance the clarity of the panoramic image. A group of experiments are illustrated from all multi-focus image fusion experiments we have done.…”

Section: Introductionmentioning

confidence: 98%

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Application of Multi-Wavelet Transform in Multi-Focus Image Fusion

Yang

2009

2009 First International Workshop on Education Technology and Computer Science

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According to the characteristics of multi-focus images, a new image fusion algorithm based on multi-wavelet transform is proposed in this paper. Firstly, in order to obtain the sub-band multi-wavelet coefficients, two multi-focus images are transformed by multi-wavelet. Then, the regional gradients of high-frequency sub-bands at the largest scale are calculated. Finally, the high-frequency multi-wavelet coefficients and lowfrequency multi-wavelet coefficients of the fusion image are determined simultaneously by the principle which is called the direction consistency of regional gradients. This algorithm can avoid the inconsistency that the fusion multi-wavelet coefficients at the same point are extracted from the source image A in the horizontal direction, and from the source image B in the vertical direction. Experiments illustrated here indicate that the new algorithm not only retains the details and edge characteristics of the source images, but also enhances visibility and clarity of the fusion image.

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Section: Experimenta Resultsmentioning

confidence: 78%

Section: Experimenta Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

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Application of Multi-Wavelet Transform in Multi-Focus Image Fusion

Yang

2009

2009 First International Workshop on Education Technology and Computer Science

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“…In the case average and standard deviation of source images are approximately equal, and we can apply the minimum rule [12]. This is not only can keep the main energy of the image, but also can get de-noising effect to some extent.…”

Section: Fusion Rules and Fusion Operatorsmentioning

confidence: 98%

“…Secondly we apply different fusion operators to fusion processing. Finally, we take the inverse wavelet transform for low-frequency and high-frequency components, which we have obtained, to reconstruct the new image, which is called "fused image"[9] [12].…”

Section: Wavelet Fusionmentioning

confidence: 99%

A New Strategy to Improve Image Fusion Effect

Liu

Yin

Guo

2006

2006 International Conference on Machine Learning and Cybernetics

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The purpose of image fusion is to combine information from several different source images to one image, which becomes reliable and much easier to be comprehended by people. Based on analyzing the relations of average and standard deviation of the two or more source images, a new strategy to improve image fusion effect and a new evaluation measure named RAS (the ratio between average and standard deviation) are proposed in this paper. We apply wavelet transform to decompose an image into low-frequency sub-image and high-frequency sub-images and apply different fusion rules respectively to low-frequency sub-image and high-frequency sub-images. According to subjective evaluation and objective criteria, such as entropy, root mean square error (RMSE), peak-to-peak signal-to-noise ratio (PSNR), RAS, the proposed strategy is very effective and universal to some extent for fusing a class of images whose average and standard deviation are approximately equal respectively through extensive experiments.

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An image fusion algorithm based on lifting wavelet transform

Wang

Shen

Zhou

et al. 2015

J. Opt.

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The directional characteristic of the low-frequency and high-frequency coefficients based on the wavelet transform for original images is discussed and analyzed, and a novel image fusion algorithm based on the lifting wavelet transform is proposed in this paper. Firstly, the source images are transformed to the frequency domain by means of the lifting wavelet. Then, the resultant coefficients of the low-frequency sub-band are achieved by comparing the covariance of the coefficients of different images. Meanwhile, the resultant coefficients of each high-frequency sub-band are calculated according to the matching measure between the directional characteristic of the coefficients in the same sub-band and the quad-tree structure relationship of the coefficients with the same direction in different sub-bands. At last, the fusion-resultant image is obtained through the reversely lifting wavelet transformation. Several evaluation indexes, such as entropy, average grads, PSNR and RMSE, are employed to judge the experimental images with different fusion methods. The comparison results show that the proposed method image fusion algorithm based on the lifting wavelet transform is better than conventional methods, and has much application value.

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Image Fusion Based on Multi-wavelet Transform

Cited by 5 publications

References 11 publications

Application of Multi-Wavelet Transform in Multi-Focus Image Fusion

Application of Multi-Wavelet Transform in Multi-Focus Image Fusion

A New Strategy to Improve Image Fusion Effect

An image fusion algorithm based on lifting wavelet transform

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