Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking

Panguluri, Sumanth Kumar; Mohan, Laavanya

doi:10.3311/ppee.14702

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…(3) DWT based unsharp masking fusion method [24]. All the three above methods are implemented and have been used for evaluating proposed method performance in this paper.…”

Section: Comparison Methodsmentioning

confidence: 99%

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A DWT Based Novel Multimodal Image Fusion Method

Panguluri¹,

Mohan²

2021

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Nowadays multimodal image fusion has been majorly utilized as an important processing tool in various image related applications. For capturing useful information different sensors have been developed. Mainly such sensors are infrared (IR) image sensor and visible (VI) image sensor. Fusing both these sensors provides better and accurate scene information. The major application areas where this fused image has been mostly used are military, surveillance, and remote sensing. For better identification of targets and to understand overall scene information, the fused image has to provide better contrast and more edge information. This paper introduces a novel multimodal image fusion method mainly for improving contrast and as well as edge information. Primary step of this algorithm is to resize source images. The 3×3 sharpen filter and morphology hat transform are applied separately on resized IR image and VI image. DWT transform has been used to produce "low-frequency" and "high-frequency" sub-bands. "Filters based mean-weighted fusion rule" and "Filters based max-weighted fusion rule" are newly introduced in this algorithm for combining "low-frequency" sub-bands and "high-frequency" sub-bands respectively. Fused image reconstruction is done with IDWT. Proposed method has outperformed and shown improved results in subjective manner and objectively than similar existing techniques.

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“…(3) DWT based unsharp masking fusion method [24]. All the three above methods are implemented and have been used for evaluating proposed method performance in this paper.…”

Section: Comparison Methodsmentioning

confidence: 99%

“…Panguluri and Mohan [24] have proposed a novel DWT based fusion method for fusing IR and VI images in 2020. The novelty of this method is that applying the unsharp masking technique in the frequency domain for integrating lowfrequency sub-bands in order to improve contrast.…”

Section: Related Workmentioning

confidence: 99%

A DWT Based Novel Multimodal Image Fusion Method

Panguluri¹,

Mohan²

2021

Self Cite

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“…Figure 8 shows the fusion algorithm, and the simulation results are shown in Figure 9. The metrics used for measuring the performance MRI image fusion algorithm, are mean, information entropy (IE), and standard deviation (SD) [24,25]. The Fusion algorithm's performance on the test MRI images is displayed in Table 3.…”

Section: Algorithm For Mri Image Fusionmentioning

confidence: 99%

Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Hamad,

Ismail,

Raed

2024

JESA

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“…Then, the processed coefficients can be converted to the fused image by inverse MST. According to the different decomposition methods of source images, MST can be divided into pyramid-based methods [2][3][4], wavelet-based methods [5][6][7][8], and multiscale geometric analysis-(MGA-) based methods [9][10][11][12][13][14][15][16][17][18]. Due to the limitation of preset functions in the MST-based algorithm, some essential features of the source images, such as edge and texture information, may not be well expressed and extracted, which significantly reduces the fusion performance.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation

Lou

Feng

2021

Computational and Mathematical Methods in Medicine

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A multimodal medical image fusion algorithm based on multiple latent low-rank representation is proposed to improve imaging quality by solving fuzzy details and enhancing the display of lesions. Firstly, the proposed method decomposes the source image repeatedly using latent low-rank representation to obtain several saliency parts and one low-rank part. Secondly, the VGG-19 network identifies the low-rank part’s features and generates the weight maps. Then, the fused low-rank part can be obtained by making the Hadamard product of the weight maps and the source images. Thirdly, the fused saliency parts can be obtained by selecting the max value. Finally, the fused saliency parts and low-rank part are superimposed to obtain the fused image. Experimental results show that the proposed method is superior to the traditional multimodal medical image fusion algorithms in the subjective evaluation and objective indexes.

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Discrete Wavelet Transform Based Image Fusion Using Unsharp Masking

Cited by 13 publications

References 19 publications

A DWT Based Novel Multimodal Image Fusion Method

A DWT Based Novel Multimodal Image Fusion Method

Efficient MRI Image Real-Time Processing Using FPGA-Based IIR Filters

Multimodal Medical Image Fusion Based on Multiple Latent Low-Rank Representation

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