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.

Image Fusion Method Based on Multi-scale Directional Fast Guided Filter and Convolutional Sparse Representation

2019 ◽  
Vol 12 (3) ◽  
pp. 263-269
Author(s):  
Liu Xian-Hong ◽  
Chen Zhi-Bin
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Filter Bank ◽  
Guided Filter ◽  
Fusion Method ◽  
Edge Preserving ◽  
Directional Information ◽  
Multi Scale ◽  
Directional Filter ◽  
Image Fusion Method

Background: A multi-scale multidirectional image fusion method is proposed, which introduces the Nonsubsampled Directional Filter Bank (NSDFB) into the multi-scale edge-preserving decomposition based on the fast guided filter. Methods: The proposed method has the advantages of preserving edges and extracting directional information simultaneously. In order to get better-fused sub-bands coefficients, a Convolutional Sparse Representation (CSR) based approximation sub-bands fusion rule is introduced and a Pulse Coupled Neural Network (PCNN) based detail sub-bands fusion strategy with New Sum of Modified Laplacian (NSML) to be the external input is also presented simultaneously. Results: Experimental results have demonstrated the superiority of the proposed method over conventional methods in terms of visual effects and objective evaluations. Conclusion: In this paper, combining fast guided filter and nonsubsampled directional filter bank, a multi-scale directional edge-preserving filter image fusion method is proposed. The proposed method has the features of edge-preserving and extracting directional information.

scholarly journals Image fusion method based on simultaneous sparse representation with non‐subsampled contourlet transform

2019 ◽  
Vol 13 (2) ◽  
pp. 240-248 ◽  
Author(s):  
Guiqing He ◽  
Siyuan Xing ◽  
Xingjian He ◽  
Jun Wang ◽  
Jianping Fan
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Contourlet Transform ◽  
Fusion Method ◽  
Image Fusion Method

Image fusion method based on spatially masked convolutional sparse representation

2019 ◽  
Vol 90 ◽  
pp. 103806 ◽  
Author(s):  
Changda Xing ◽  
Zhisheng Wang ◽  
Quan Ouyang ◽  
Chong Dong ◽  
Chaowei Duan
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Fusion Method ◽  
Image Fusion Method

scholarly journals Multi-focus Image Fusion Based on Decision Map and Sparse Representation

2019 ◽  
Vol 9 (17) ◽  
pp. 3612
Author(s):  
Liao ◽  
Chen ◽  
Mo
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Focal Length ◽  
The Other ◽  
Fusion Method ◽  
Optical Lens ◽  
Transitional Area ◽  
Fusion Methods ◽  
Focus Image ◽  
Image Fusion Method

As the focal length of an optical lens in a conventional camera is limited, it is usually arduous to obtain an image in which each object is focused. This problem can be solved by multi-focus image fusion. In this paper, we propose an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR). First, we obtained a decision map by analyzing low-scale images with sparse representation, measuring the effective clarity level, and using spatial frequency methods to process uncertain areas. Subsequently, the transitional area around the focus boundary was determined by the decision map, and we implemented the transitional area fusion based on sparse representation. The experimental results show that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation.

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 New Deep Learning Based Multi-Spectral Image Fusion Method

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 570 ◽  
Author(s):  
Jingchun Piao ◽  
Yunfan Chen ◽  
Hyunchul Shin
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Pedestrian Detection ◽  
Fusion Rule ◽  
Activity Level ◽  
Fusion Method ◽  
Multi Scale ◽  
Level Measurement ◽  
Automatic Encoding ◽  
Image Fusion Method

In this paper, we present a new effective infrared (IR) and visible (VIS) image fusion method by using a deep neural network. In our method, a Siamese convolutional neural network (CNN) is applied to automatically generate a weight map which represents the saliency of each pixel for a pair of source images. A CNN plays a role in automatic encoding an image into a feature domain for classification. By applying the proposed method, the key problems in image fusion, which are the activity level measurement and fusion rule design, can be figured out in one shot. The fusion is carried out through the multi-scale image decomposition based on wavelet transform, and the reconstruction result is more perceptual to a human visual system. In addition, the visual qualitative effectiveness of the proposed fusion method is evaluated by comparing pedestrian detection results with other methods, by using the YOLOv3 object detector using a public benchmark dataset. The experimental results show that our proposed method showed competitive results in terms of both quantitative assessment and visual quality.

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