Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed...Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.展开更多
Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus ...Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.展开更多
In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer p...In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer pattern images are fused before colorrestoration. Therefore, the Bayer image restoration operation is only performed one time. Thus, the proposed algorithm is moreefficient than traditional fusion schemes. In detail, a clarity measurement of Bayer pattern image is defined for raw Bayer patternimages, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. Theraw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicingalgorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain betterfused results with more natural appearance and fewer artifacts than the traditional algorithms.展开更多
A multi-focus optical fiber lens is numerically demonstrated based on an all-dielectric metasurface structure.The metasurface consists of an array of rectangular silicon resonators with varying widths in order to obta...A multi-focus optical fiber lens is numerically demonstrated based on an all-dielectric metasurface structure.The metasurface consists of an array of rectangular silicon resonators with varying widths in order to obtain the required phase distribution.The core diameter of the multimode fiber is large enough to contain sufficient resonance units.The spatial distribution of the dielectric resonators is dictated by spatial multiplexing,including interleaving meta-atoms and lens aperture division,to achieve multi-focus properties.The proposed optical fiber metalens can produce two or three focal points along the longitudinal direction with high focusing efficiency.The size of every focal point is close to the diffraction limit,and the relative intensity on each focus can be controlled by adjusting the number of the respective resonators.The proposed optical fiber lens will have a great potential in the fields of integrated optics and multifunctional micro/nano devices.展开更多
Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order de...Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order derivative and intuitionistic fuzzy sets.The original image is decomposed into a base layer and a detail layer.Furthermore,a new fractional-order spatial frequency is built to reflect the clarity of the image.The fractional-order spatial frequency is used as a rule for detail layers fusion,and intuitionistic fuzzy sets are introduced to fuse base layers.Experimental results demonstrate that the proposed fusion method outperforms the state-of-the-art methods for multi-focus image fusion.展开更多
A novel multi-focus polychromatic image fusion algorithm based on filtering in the frequency domain using fast Fourier transform(FFT) and synthesis in the space domain(FFDSSD) is presented in this paper.First,the orig...A novel multi-focus polychromatic image fusion algorithm based on filtering in the frequency domain using fast Fourier transform(FFT) and synthesis in the space domain(FFDSSD) is presented in this paper.First,the original multi-focus images are transformed into their frequency data by FFT for easy and accurate clarity determination.Then a Gaussian low-pass filter is used to filter the high frequency information corresponding to the image saliencies.After an inverse FFT,the filtered images are obtained.The deviation between the filtered images and the original ones,representing the clarity of the image,is used to select the pixels from the multi-focus images to reconstruct a completely focused image.These operations in space domain preserve the original information as much as possible and are relatively insensitive to misregistration scenarios with respect to transform domain methods.The polychromatic noise is well considered and successfully avoided while the information in different chromatic channels is preserved.A natural,nice-looking fused microscopic image for human visual evaluations is obtained in a dedicated experiment.The experimental results indicate that the proposed algorithm has a good performance in objective quality metrics and runtime efficiency.展开更多
A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image f...A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.展开更多
We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the networ...We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.展开更多
文摘Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.
文摘Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.
文摘In this paper, a joint multifocus image fusion and Bayer pattern image restoration algorithm for raw images of single-sensor colorimaging devices is proposed. Different from traditional fusion schemes, the raw Bayer pattern images are fused before colorrestoration. Therefore, the Bayer image restoration operation is only performed one time. Thus, the proposed algorithm is moreefficient than traditional fusion schemes. In detail, a clarity measurement of Bayer pattern image is defined for raw Bayer patternimages, and the fusion operator is performed on superpixels which provide powerful grouping cues of local image feature. Theraw images are merged with refined weight map to get the fused Bayer pattern image, which is restored by the demosaicingalgorithm to get the full resolution color image. Experimental results demonstrate that the proposed algorithm can obtain betterfused results with more natural appearance and fewer artifacts than the traditional algorithms.
基金the National Natural Science Foundation of China(NSFC)(Nos.91750107,61675054,and U1931121)Natural Science Foundation of Heilongjiang Province,China(Nos.ZD2018015 and ZD2020F002)+1 种基金111 Project of the Harbin Engineering University(No.B13015)Fundamental Research Funds for Harbin Engineering University of China(Nos.3072020CFT2504 and 3072020CFT2501).
文摘A multi-focus optical fiber lens is numerically demonstrated based on an all-dielectric metasurface structure.The metasurface consists of an array of rectangular silicon resonators with varying widths in order to obtain the required phase distribution.The core diameter of the multimode fiber is large enough to contain sufficient resonance units.The spatial distribution of the dielectric resonators is dictated by spatial multiplexing,including interleaving meta-atoms and lens aperture division,to achieve multi-focus properties.The proposed optical fiber metalens can produce two or three focal points along the longitudinal direction with high focusing efficiency.The size of every focal point is close to the diffraction limit,and the relative intensity on each focus can be controlled by adjusting the number of the respective resonators.The proposed optical fiber lens will have a great potential in the fields of integrated optics and multifunctional micro/nano devices.
文摘Multi-focus image fusion is an increasingly important component in image fusion,and it plays a key role in imaging.In this paper,we put forward a novel multi-focus image fusion method which employs fractional-order derivative and intuitionistic fuzzy sets.The original image is decomposed into a base layer and a detail layer.Furthermore,a new fractional-order spatial frequency is built to reflect the clarity of the image.The fractional-order spatial frequency is used as a rule for detail layers fusion,and intuitionistic fuzzy sets are introduced to fuse base layers.Experimental results demonstrate that the proposed fusion method outperforms the state-of-the-art methods for multi-focus image fusion.
文摘A novel multi-focus polychromatic image fusion algorithm based on filtering in the frequency domain using fast Fourier transform(FFT) and synthesis in the space domain(FFDSSD) is presented in this paper.First,the original multi-focus images are transformed into their frequency data by FFT for easy and accurate clarity determination.Then a Gaussian low-pass filter is used to filter the high frequency information corresponding to the image saliencies.After an inverse FFT,the filtered images are obtained.The deviation between the filtered images and the original ones,representing the clarity of the image,is used to select the pixels from the multi-focus images to reconstruct a completely focused image.These operations in space domain preserve the original information as much as possible and are relatively insensitive to misregistration scenarios with respect to transform domain methods.The polychromatic noise is well considered and successfully avoided while the information in different chromatic channels is preserved.A natural,nice-looking fused microscopic image for human visual evaluations is obtained in a dedicated experiment.The experimental results indicate that the proposed algorithm has a good performance in objective quality metrics and runtime efficiency.
基金supported by the National Natural Science Foundation of China(No.61174193)
文摘A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.
基金Project supported by the National Natural Science Foundation of China(No.61801190)the Natural Science Foundation of Jilin Province,China(No.20180101055JC)the Outstanding Young Talent Foundation of Jilin Province,China(No.20180520029JH)。
文摘We propose a multi-focus image fusion method, in which a fully convolutional network for focus detection(FD-FCN) is constructed. To obtain more precise focus detection maps, we propose to add skip layers in the network to make both detailed and abstract visual information available when using FD-FCN to generate maps. A new training dataset for the proposed network is constructed based on dataset CIFAR-10. The image fusion algorithm using FD-FCN contains three steps: focus maps are obtained using FD-FCN, decision map generation occurs by applying a morphological process on the focus maps, and image fusion occurs using a decision map. We carry out several sets of experiments, and both subjective and objective assessments demonstrate the superiority of the proposed fusion method to state-of-the-art algorithms.
