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Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform

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Abstract

The energy compaction and multiresolution properties of wavelets have made the image fusion successful in combining important features such as edges and textures from source images without introducing any artifacts for context enhancement and situational awareness. The wavelet transform is visualized as a convolution of wavelet filter coefficients with the image under consideration and is computationally intensive. The advent of lifting-based wavelets has reduced the computations but at the cost of visual quality and performance of the fused image. To retain the visual quality and performance of the fused image with reduced computations, a discrete cosine harmonic wavelet (DCHWT)-based image fusion is proposed. The performance of DCHWT is compared with both convolution and lifting-based image fusion approaches. It is found that the performance of DCHWT is similar to convolution-based wavelets and superior/similar to lifting-based wavelets. Also, the computational complexity (in terms of additions and multiplications) of the proposed method scores over convolution-based wavelets and is competitive to lifting-based wavelets.

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Abbreviations

IR:

Infrared

DWT:

Discrete wavelet transform

WT:

Wavelet transform

MDCT:

Multiresolution discrete cosine transform

HWT:

Harmonic wavelet transform

FT:

Fourier transform

FFT:

Fast Fourier transform

DFT:

Discrete Fourier transform

HWC:

Harmonic wavelet coefficient

DCT:

Discrete cosine transform

IDCT:

Inverse DCT

DCTHWT:

Discrete cosine transform harmonic wavelet transform

DCHWT:

Discrete cosine harmonic wavelet transform

DCHWC:

Discrete cosine harmonic wavelet coefficients

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Shreyamsha Kumar, B.K. Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform. SIViP 7, 1125–1143 (2013). https://doi.org/10.1007/s11760-012-0361-x

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