Multi-focus image fusion based on joint sparse representation and optimum theory

Ma, Xiaole; Hu, Shaohai; Liu, Shuaiqi; Fang, Jing; Xu, Shuwen

doi:10.1016/j.image.2019.06.002

Cited by 45 publications

(18 citation statements)

References 40 publications

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“…This strategy has low training cost, is easy to operate, and is suitable for the sparse representation of multiple types of images. The other strategy is to use a fixed dictionary for common components and an adaptive dictionary for innovation components for sparse coding and reconstruction [ 27 ]. This strategy may yield better results, but comes with additional computational costs.…”

Section: Related Workmentioning

confidence: 99%

“…Yu et al proposed a JSR-based approach to carry out image denoising and fusion simultaneously [ 26 ]. Ma et al combined JSR and optimum theory to address the multi-focus image fusion problem [ 27 ]. However, including JSR, all SR-based methods generally have the following disadvantages: (1) an over-complete dictionary may result in visual artifacts in the reconstructed image; (2) simple fusion strategy for sparse coefficient vectors leads to spatial inconsistency.…”

Section: Introductionmentioning

confidence: 99%

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Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan

Yang

Zhang

et al. 2020

Entropy

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Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ( M I )—5.3377, feature mutual information ( F M I )—0.5600, normalized weighted edge preservation value ( Q A B / F )—0.6978 and nonlinear correlation information entropy ( N C I E )—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan

Yang

Zhang

et al. 2020

Entropy

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“…Multi-focus image fusion technology is used to extract different focus areas from multiple images in the same scene to synthesize a clear image. The fusion process can also be regarded to improve the image quality on image processing [2], since clear all-focus images are more suitable for human visual perception systems and post-processing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

Self Cite

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Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its superresolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes. INDEX TERMS Multi-focus image fusion, super-resolution, dual convolutional neural network, focus area detection.

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“…This technology mainly uses the edge information of the sparse matrix for fusion. Ma et al [24] proposed a multifocus image fusion method, mainly established in one fusion rule of sparse coefficients, which is based on the optimum theory and solved by the orthogonal matching pursuit method. Wang and Bai [25] proposed a novel strategy on the low frequency fusion assisted through sparse representation.…”

Section: Introductionmentioning

confidence: 99%

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Jian

Chu

et al. 2020

Applied Sciences

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This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

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Multi-focus image fusion based on joint sparse representation and optimum theory

Cited by 45 publications

References 40 publications

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

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