A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation
Multi-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/10/10/129 |
| _version_ | 1797514951887159296 |
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| author | Guanqiu Qi Gang Hu Neal Mazur Huahua Liang Matthew Haner |
| author_facet | Guanqiu Qi Gang Hu Neal Mazur Huahua Liang Matthew Haner |
| author_sort | Guanqiu Qi |
| collection | DOAJ |
| description | Multi-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fundamental method of noisy image fusion, source images are denoised first, and then the denoised images are fused. However, image denoising can decrease the sharpness of source images to affect the fusion performance. Additionally, denoising and fusion are processed in separate processing modes, which causes an increase in computation cost. To fuse noisy multi-modality image pairs accurately and efficiently, a multi-modality image simultaneous fusion and denoising method is proposed. In the proposed method, noisy source images are decomposed into cartoon and texture components. Cartoon-texture decomposition not only decomposes source images into detail and structure components for different image fusion schemes, but also isolates image noise from texture components. A Gaussian scale mixture (GSM) based sparse representation model is presented for the denoising and fusion of texture components. A spatial domain fusion rule is applied to cartoon components. The comparative experimental results confirm the proposed simultaneous image denoising and fusion method is superior to the state-of-the-art methods in terms of visual and quantitative evaluations. |
| first_indexed | 2024-03-10T06:38:49Z |
| format | Article |
| id | doaj.art-b0078e046da3417e9049920059cc51bb |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T06:38:49Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-b0078e046da3417e9049920059cc51bb2023-11-22T17:52:20ZengMDPI AGComputers2073-431X2021-10-01101012910.3390/computers10100129A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse RepresentationGuanqiu Qi0Gang Hu1Neal Mazur2Huahua Liang3Matthew Haner4Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USAComputer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USAComputer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USACollege of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaDepartment of Mathematics & Computer and Information Science, Mansfield University of Pennsylvania, Mansfield, PA 16933, USAMulti-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fundamental method of noisy image fusion, source images are denoised first, and then the denoised images are fused. However, image denoising can decrease the sharpness of source images to affect the fusion performance. Additionally, denoising and fusion are processed in separate processing modes, which causes an increase in computation cost. To fuse noisy multi-modality image pairs accurately and efficiently, a multi-modality image simultaneous fusion and denoising method is proposed. In the proposed method, noisy source images are decomposed into cartoon and texture components. Cartoon-texture decomposition not only decomposes source images into detail and structure components for different image fusion schemes, but also isolates image noise from texture components. A Gaussian scale mixture (GSM) based sparse representation model is presented for the denoising and fusion of texture components. A spatial domain fusion rule is applied to cartoon components. The comparative experimental results confirm the proposed simultaneous image denoising and fusion method is superior to the state-of-the-art methods in terms of visual and quantitative evaluations.https://www.mdpi.com/2073-431X/10/10/129sparse representationnoisy image fusioncartoon-texture decompositionsimultaneous image denoising and fusion |
| spellingShingle | Guanqiu Qi Gang Hu Neal Mazur Huahua Liang Matthew Haner A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation Computers sparse representation noisy image fusion cartoon-texture decomposition simultaneous image denoising and fusion |
| title | A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation |
| title_full | A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation |
| title_fullStr | A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation |
| title_full_unstemmed | A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation |
| title_short | A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation |
| title_sort | novel multi modality image simultaneous denoising and fusion method based on sparse representation |
| topic | sparse representation noisy image fusion cartoon-texture decomposition simultaneous image denoising and fusion |
| url | https://www.mdpi.com/2073-431X/10/10/129 |
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