A Multispectral and Panchromatic Images Fusion Method Based on Weighted Mean Curvature Filter Decomposition

Since the hardware limitations of satellite sensors, the spatial resolution of multispectral (MS) images is still not consistent with the panchromatic (PAN) images. It is especially important to obtain the MS images with high spatial resolution in the field of remote sensing image fusion. In order to obtain the MS images with high spatial and spectral resolutions, a novel MS and PAN images fusion method based on weighted mean curvature filter (WMCF) decomposition is proposed in this paper. Firstly, a weighted local spatial frequency-based (WLSF) fusion method is utilized to fuse all the bands of a MS image to generate an intensity component IC. In accordance with an image matting model, IC is taken as the original α channel for spectral estimation to obtain a foreground and background images. Secondly, a PAN image is decomposed into a small-scale (SS), large-scale (LS) and basic images by weighted mean curvature filter (WMCF) and Gaussian filter (GF). The multi-scale morphological detail measure (MSMDM) value is used as the inputs of the Parameters Automatic Calculation Pulse Coupled Neural Network (PAC-PCNN) model. With the MSMDM-guided PAC-PCNN model, the basic image and IC are effectively fused. The fused image as well as the LS and SS images are linearly combined together to construct the last α channel. Finally, in accordance with an image matting model, a foreground image, a background image and the last α channel are reconstructed to acquire the final fused image. The experimental results on four image pairs show that the proposed method achieves superior results in terms of subjective and objective evaluations. In particular, the proposed method can fuse MS and PAN images with different spatial and spectral resolutions in a higher operational efficiency, which is an effective means to obtain higher spatial and spectral resolution images

A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution

A Pan-Sharpening Method with Beta-Divergence Non-Negative Matrix Factorization in Non-Subsampled Shear Transform Domain

In order to combine the spectral information of the multispectral (MS) image and the spatial information of the panchromatic (PAN) image, a pan-sharpening method based on β-divergence Non-negative Matrix Factorization (NMF) in the Non-Subsampled Shearlet Transform (NSST) domain is proposed. Firstly, we improve the traditional contrast calculation method to build the weighted local contrast measure (WLCM) method. Each band of the MS image is fused by a WLCM-based adaptive weighted averaging rule to obtain the intensity component I. Secondly, an image matting model is introduced to retain the spectral information of the MS image. I is used as the initial α channel to estimate the foreground color F and the background color B. Depending on the NSST, the PAN image and I are decomposed into one low-frequency component and several high-frequency components, respectively. Fusion rules are designed corresponding to the characteristics of the low-frequency and high-frequency components. A β-divergence NMF method based on the Alternating Direction Method of Multipliers (ADMM) is used to fuse the low frequency components. A WLCM-based rule is used to fuse the high-frequency components. The fused components are inverted by NSST inverse transformation, and the obtained image is used as the final α channel. Finally, the final fused image is reconstructed according to the foreground color F, background color B, and the final α channel. The experimental results demonstrate that the proposed method achieves superior performance in both subjective visual effects and objective evaluation, and effectively preserves spectral information while improving spatial resolution

A Multispectral and Panchromatic Images Fusion Method Based on Weighted Mean Curvature Filter Decomposition

Since the hardware limitations of satellite sensors, the spatial resolution of multispectral (MS) images is still not consistent with the panchromatic (PAN) images. It is especially important to obtain the MS images with high spatial resolution in the field of remote sensing image fusion. In order to obtain the MS images with high spatial and spectral resolutions, a novel MS and PAN images fusion method based on weighted mean curvature filter (WMCF) decomposition is proposed in this paper. Firstly, a weighted local spatial frequency-based (WLSF) fusion method is utilized to fuse all the bands of a MS image to generate an intensity component IC. In accordance with an image matting model, IC is taken as the original α channel for spectral estimation to obtain a foreground and background images. Secondly, a PAN image is decomposed into a small-scale (SS), large-scale (LS) and basic images by weighted mean curvature filter (WMCF) and Gaussian filter (GF). The multi-scale morphological detail measure (MSMDM) value is used as the inputs of the Parameters Automatic Calculation Pulse Coupled Neural Network (PAC-PCNN) model. With the MSMDM-guided PAC-PCNN model, the basic image and IC are effectively fused. The fused image as well as the LS and SS images are linearly combined together to construct the last α channel. Finally, in accordance with an image matting model, a foreground image, a background image and the last α channel are reconstructed to acquire the final fused image. The experimental results on four image pairs show that the proposed method achieves superior results in terms of subjective and objective evaluations. In particular, the proposed method can fuse MS and PAN images with different spatial and spectral resolutions in a higher operational efficiency, which is an effective means to obtain higher spatial and spectral resolution images

Exploration and Application of Microsurgical Training in Young Neurosurgeons at Peking Union Medical College Hospital

Microsurgical technique is essential for neurosurgeons, which demands good hand-eye coordination and fine motor skills. It is therefore necessary to provide systematic microsurgical training for young doctors with limited clinical experience. However, there is currently a lack of unified training programs in China. Based on clinical needs, the Neurosurgery Department of Peking Union Medical College Hospital has developed an integrated, immersive, and high-intensity microsurgical training course that covers training closely related to clinical practice, such as the use of microsurgical instruments, suture of gauze holes, anastomosis of artificial blood vessels, anastomosis of middle cerebral artery, and the use of micro drills. Since March 2022, six training sessions have been completed with a total of 12 trainees, and all have passed the assessment. Four of them have been recognized as microsurgical main surgeons. Preliminary data shows that this training course has helped to improve the microsurgical skills of young neurosurgeons, providing a reference for future microsurgical training programs

Manipulating efficient light emission in two-dimensional perovskite crystals by pressure-induced anisotropic deformation

The hybrid nature and soft lattice of organolead halide perovskites render their structural changes and optical properties susceptible to external driving forces such as temperature and pressure, remarkably different from conventional semiconductors. Here, we investigate the pressure-induced optical response of a typical two-dimensional perovskite crystal, phenylethylamine lead iodide. At a moderate pressure within 3.5 GPa, its photoluminescence red-shifts continuously, exhibiting an ultrabroad energy tunability range up to 320 meV in the visible spectrum, with quantum yield remaining nearly constant. First-principles calculations suggest that an out-of-plane quasi-uniaxial compression occurs under a hydrostatic pressure, while the energy is absorbed by the reversible and elastic tilting of the benzene rings within the long-chain ligands. This anisotropic structural deformation effectively modulates the quantum confinement effect by 250 meV via barrier height lowering. The broad tunability within a relatively low pressure range will expand optoelectronic applications to a new paradigm with pressure as a tuning knob.ASTAR (Agency for Sci., Tech. and Research, S’pore)MOE (Min. of Education, S’pore)Published versio