ICTACT Journal on Image and Video Processing

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Multi-Focus Image Fusion Method with QshiftN-DTCWT and Modified PCA in Frequency Partition Domain


Affiliations
1 Department of Computer Science Engineering, Visvesvaraya Technological University, India
2 Department of Computer Science Engineering, YSR Engineering College of Yogi Vemana University, India
3 Department of Computer Science Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, India
4 Department of Physics, YSR Engineering College of Yogi Vemana University, India
     

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Multi-focus imaging fusion is a technique that puts together a fully focused object from the partly focused regions of several objects from the same scene. For producing a high quality fused image, directional selectivity and invariance characteristics are important. The ringed artifacts, however, were inserted into a fused image because of a lack of invariance and misdirection. A multi-focus image fusion algorithm is proposed to resolve these issues, in conjunction with qshiftN dual-tree complex wavelet transform and modified principal component analysis. First, the source images are translated into the FP domain. It helps in the obtaining of the row frequency components and column frequency components. Then the row-frequency elements and column-frequency elements are combined with a dual tree-complex wavelet qshiftN to transform the origin frames. Dual tree complex wavelet transforms with qshiftN has demonstrated that it provides an effective transformation for multi-resolution imaging fusion with its directional and shift-invariant characteristics. To enlarge the effectiveness of the qshiftN dual-tree complex wavelet transform in frequency partition-based method, the modified principal component analysis (MPCA) algorithm is used. The proposed fusion approach has been tested on a numeral of multi-focus images and compared to various popular methods of imaging fusion. The experimental results indicate that in subjective performance and objective assessment, the proposed fusion approach could deliver better fusion results.

Keywords

Multi-focus Image Fusion, Multi-resolution Transform, qshiftN Dual Tree Complex Wavelet Transform, Modified Principal Component Analysis, Quality Evaluation Metrics.
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  • Multi-Focus Image Fusion Method with QshiftN-DTCWT and Modified PCA in Frequency Partition Domain

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Authors

C. Rama Mohan
Department of Computer Science Engineering, Visvesvaraya Technological University, India
S. Kiran
Department of Computer Science Engineering, YSR Engineering College of Yogi Vemana University, India
Vasudeva
Department of Computer Science Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, India
A. Ashok Kumar
Department of Physics, YSR Engineering College of Yogi Vemana University, India

Abstract


Multi-focus imaging fusion is a technique that puts together a fully focused object from the partly focused regions of several objects from the same scene. For producing a high quality fused image, directional selectivity and invariance characteristics are important. The ringed artifacts, however, were inserted into a fused image because of a lack of invariance and misdirection. A multi-focus image fusion algorithm is proposed to resolve these issues, in conjunction with qshiftN dual-tree complex wavelet transform and modified principal component analysis. First, the source images are translated into the FP domain. It helps in the obtaining of the row frequency components and column frequency components. Then the row-frequency elements and column-frequency elements are combined with a dual tree-complex wavelet qshiftN to transform the origin frames. Dual tree complex wavelet transforms with qshiftN has demonstrated that it provides an effective transformation for multi-resolution imaging fusion with its directional and shift-invariant characteristics. To enlarge the effectiveness of the qshiftN dual-tree complex wavelet transform in frequency partition-based method, the modified principal component analysis (MPCA) algorithm is used. The proposed fusion approach has been tested on a numeral of multi-focus images and compared to various popular methods of imaging fusion. The experimental results indicate that in subjective performance and objective assessment, the proposed fusion approach could deliver better fusion results.

Keywords


Multi-focus Image Fusion, Multi-resolution Transform, qshiftN Dual Tree Complex Wavelet Transform, Modified Principal Component Analysis, Quality Evaluation Metrics.

References