scholarly journals SAR image denoising method based on sparse representation

2019 ◽  
Vol 2019 (20) ◽  
pp. 7153-7156 ◽  
Author(s):  
Hao-Tian Zhou ◽  
Liang Chen ◽  
Bo Fu ◽  
Hao Shi
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Sar Image ◽  
Denoising Method

scholarly journals A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 129
Author(s):  
Guanqiu Qi ◽  
Gang Hu ◽  
Neal Mazur ◽  
Huahua Liang ◽  
Matthew Haner
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Image Denoising ◽  
Fusion Rule ◽  
Fusion Method ◽  
Denoising Method ◽  
Gaussian Scale Mixture ◽  
Processing Modes ◽  
Image Pairs ◽  
Imaging Sensors

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.

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