Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the r...Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.展开更多
Based on the Fresnel-Kirchhoff diffraction theory, we build up a Gaussian diffraction model of metal-oxide-type super-resolution near field structure (super-liENS), which can describe far field optical properties. T...Based on the Fresnel-Kirchhoff diffraction theory, we build up a Gaussian diffraction model of metal-oxide-type super-resolution near field structure (super-liENS), which can describe far field optical properties. The spectral contrast induced by refractive index and the structural changes in AgOg, PtOx and PdOz thin films, which are the key functional layers in super-RENS, are studied by using this model. Comparison results indicate that the spectral contrast depends intensively on the laser-induced distribution and change of the refractive index in the metal-oxide films. The readout mechanism of the metal-oxide-type super-RENS optical disc is further clarified. This Gaussian diffraction model can be used as a simple and effective method for choosing proper active materials in super-RENS.展开更多
A novel metallized azo dye has been synthesized. The absorption spectra of the thin film and thermal characteristic are measured. Static optical recording properties with and without the Bi mask layer super-resolution...A novel metallized azo dye has been synthesized. The absorption spectra of the thin film and thermal characteristic are measured. Static optical recording properties with and without the Bi mask layer super-resolution near-field structure (Super-RENS) of the metal-azo dye are investigated. The results show that the metal-azo dye film has a broad absorbance band in the region of 450-650 nm and the maximum absorbance wavelength is located at 603 nm. It is also found that the new metallized azo dye occupies excellent thermal stability, initiatory decomposition temperature is at 270℃ and the mass loss is about 48% in a narrow temperature region (15℃). The complex refractive index N (N = n + iκ) is measured. High refractive index (n = 2.45) and low extinction coefficient (κ = 0.2) at the recording wavelength 65Ohm are attained. Static optical recording tests with and without Super-RENS are carried out using a 65Ohm semiconductor diode laser with recording power of 7mW and laser pulse duration of 200ns. The AFM images show that the diameter of recording mark on the dye film with the Bi mask layer is reduced about 42%, compared to that of recorded mark on the dye film without Super-RENS. It is indicated that Bi can well performed as a mask layer of the dye recording layer and the metallized azo dye can be a promising candidate for recording media with the super-resolution near-field structure.展开更多
Traditional MEMS (microelectromechanical system) design methodology is not a structured method and has become an obstacle for MEMS creative design. In this paper, a novel method of mask synthesis and verification fo...Traditional MEMS (microelectromechanical system) design methodology is not a structured method and has become an obstacle for MEMS creative design. In this paper, a novel method of mask synthesis and verification for surface micro-machined MEMS is proposed, which is based on the geometric model of a MEMS device. The emphasis is focused on synthesizing the masks at the basis of the layer model generated from the geometric model of the MEMS device. The method is comprised of several steps: the correction of the layer model, the generation of initial masks and final masks including multi-layer etch masks, and mask simulation. Finally some test resuhs are given.展开更多
The layer transfer process is one of the most promising methods for low-cost and highly-efficient solar cells, in which transferrable mono-crystalline silicon thin wafers or films can be produced directly from gaseous...The layer transfer process is one of the most promising methods for low-cost and highly-efficient solar cells, in which transferrable mono-crystalline silicon thin wafers or films can be produced directly from gaseous feed-stocks. In this work, we show an approach to preparing seeded substrates for layer-transferrable silicon films. The commercial silicon wafers are used as mother substrates, on which periodically patterned silicon rod arrays are fabricated, and all of the surfaces of the wafers and rods are sheathed by thermal silicon oxide. Thermal evaporated aluminum film is used to fill the gaps between the rods and as the stiff mask, while polymethyl methacrylate (PMMA) and photoresist are used as the soft mask to seal the gap between the filled aluminum and the rods. Under the joint resist of the stiff and soft masks, the oxide on the rod head is selectively removed by wet etching and the seed site is formed on the rod head. The seeded substrate is obtained after the removal of the masks. This joint mask technique will promote the endeavor of the exploration of mechanically stable, unlimitedly reusable substrates for the kerfless technology.展开更多
Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning...Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning models to achieve STVSR.They first interpolate intermediate frame features between given frames,then perform local and global refinement among the feature sequence,and finally increase the spatial resolutions of these features.However,in the most important feature interpolation phase,they only capture spatial-temporal information from the most adjacent frame features,ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity.In this paper,we propose a novel long-term temporal feature aggregation network(LTFA-Net)for STVSR.Specifically,we design a long-term mixture of experts(LTMoE)module for feature interpolation.LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features,which are then combined with different weights to obtain interpolation results using several gating nets.Next,we perform local and global feature refinement using the Locally-temporal Feature Comparison(LFC)module and bidirectional deformable ConvLSTM layer,respectively.Experimental results on two standard benchmarks,Adobe240 and GoPro,indicate the effectiveness and superiority of our approach over state of the art.展开更多
基金supported by Balochistan University of Engineering and Technology,Khuzdar,Balochistan,Pakistan.
文摘Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.
基金Supported by the National Natural Science Foundation of China under Grant Nos 60207005, 60490290, 60507009, and 50672108 the Science and Technology Committee of Shanghai under Grant No 06DJ14007.
文摘Based on the Fresnel-Kirchhoff diffraction theory, we build up a Gaussian diffraction model of metal-oxide-type super-resolution near field structure (super-liENS), which can describe far field optical properties. The spectral contrast induced by refractive index and the structural changes in AgOg, PtOx and PdOz thin films, which are the key functional layers in super-RENS, are studied by using this model. Comparison results indicate that the spectral contrast depends intensively on the laser-induced distribution and change of the refractive index in the metal-oxide films. The readout mechanism of the metal-oxide-type super-RENS optical disc is further clarified. This Gaussian diffraction model can be used as a simple and effective method for choosing proper active materials in super-RENS.
基金Supported by the National Natural Science Foundation of China under Grant No 60490290.
文摘A novel metallized azo dye has been synthesized. The absorption spectra of the thin film and thermal characteristic are measured. Static optical recording properties with and without the Bi mask layer super-resolution near-field structure (Super-RENS) of the metal-azo dye are investigated. The results show that the metal-azo dye film has a broad absorbance band in the region of 450-650 nm and the maximum absorbance wavelength is located at 603 nm. It is also found that the new metallized azo dye occupies excellent thermal stability, initiatory decomposition temperature is at 270℃ and the mass loss is about 48% in a narrow temperature region (15℃). The complex refractive index N (N = n + iκ) is measured. High refractive index (n = 2.45) and low extinction coefficient (κ = 0.2) at the recording wavelength 65Ohm are attained. Static optical recording tests with and without Super-RENS are carried out using a 65Ohm semiconductor diode laser with recording power of 7mW and laser pulse duration of 200ns. The AFM images show that the diameter of recording mark on the dye film with the Bi mask layer is reduced about 42%, compared to that of recorded mark on the dye film without Super-RENS. It is indicated that Bi can well performed as a mask layer of the dye recording layer and the metallized azo dye can be a promising candidate for recording media with the super-resolution near-field structure.
基金Project supported by the National Natural Science Foundation of China (Nos. 60273057 and 60403049) and the National Basic Re-search Program (973) of China (No. 2002CB312106)
文摘Traditional MEMS (microelectromechanical system) design methodology is not a structured method and has become an obstacle for MEMS creative design. In this paper, a novel method of mask synthesis and verification for surface micro-machined MEMS is proposed, which is based on the geometric model of a MEMS device. The emphasis is focused on synthesizing the masks at the basis of the layer model generated from the geometric model of the MEMS device. The method is comprised of several steps: the correction of the layer model, the generation of initial masks and final masks including multi-layer etch masks, and mask simulation. Finally some test resuhs are given.
基金Project supported by the National Natural Science Foundation of China(Grant No.11374313)the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.11504392)
文摘The layer transfer process is one of the most promising methods for low-cost and highly-efficient solar cells, in which transferrable mono-crystalline silicon thin wafers or films can be produced directly from gaseous feed-stocks. In this work, we show an approach to preparing seeded substrates for layer-transferrable silicon films. The commercial silicon wafers are used as mother substrates, on which periodically patterned silicon rod arrays are fabricated, and all of the surfaces of the wafers and rods are sheathed by thermal silicon oxide. Thermal evaporated aluminum film is used to fill the gaps between the rods and as the stiff mask, while polymethyl methacrylate (PMMA) and photoresist are used as the soft mask to seal the gap between the filled aluminum and the rods. Under the joint resist of the stiff and soft masks, the oxide on the rod head is selectively removed by wet etching and the seed site is formed on the rod head. The seeded substrate is obtained after the removal of the masks. This joint mask technique will promote the endeavor of the exploration of mechanically stable, unlimitedly reusable substrates for the kerfless technology.
文摘Space-time video super-resolution(STVSR)serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts.Recent approaches utilize end-to-end deep learning models to achieve STVSR.They first interpolate intermediate frame features between given frames,then perform local and global refinement among the feature sequence,and finally increase the spatial resolutions of these features.However,in the most important feature interpolation phase,they only capture spatial-temporal information from the most adjacent frame features,ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity.In this paper,we propose a novel long-term temporal feature aggregation network(LTFA-Net)for STVSR.Specifically,we design a long-term mixture of experts(LTMoE)module for feature interpolation.LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features,which are then combined with different weights to obtain interpolation results using several gating nets.Next,we perform local and global feature refinement using the Locally-temporal Feature Comparison(LFC)module and bidirectional deformable ConvLSTM layer,respectively.Experimental results on two standard benchmarks,Adobe240 and GoPro,indicate the effectiveness and superiority of our approach over state of the art.