Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have ...Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have emerged.Compared to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single image.In addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual performance.In this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics.Then,we review the improved network structures and loss functions of GAN-based perceptual SISR.Subsequently,the advantages and disadvantages of different networks are analyzed by multiple comparative experiments.Finally,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.展开更多
Aqueous metal-H_(2)O_(2)cells are emerging as power batteries because of their large theoretical energy densities and multiple application scenarios,especially in underwater environments.However,the peak power densiti...Aqueous metal-H_(2)O_(2)cells are emerging as power batteries because of their large theoretical energy densities and multiple application scenarios,especially in underwater environments.However,the peak power densities are less than 300 mW cm^(-2)for most reported metal-H_(2)O_(2)cells based on Mg/Al or their alloys due to the self-corrosion.Herein,we reported a Zn-H_(2)O_(2)cell with ultrafine bean-pod-like ZnCo/N-doped electrocatalysts that were synthesized via multifunctional single-cell-chain biomass.The electrocatalyst provides abundant active sites on the crinkly interface and offers a shortened pathway for electron/ion transfer due to the desired root-like carbon nanotube(CNT)arrays.Therefore,the optimized electrocatalyst exhibited outstanding oxygen reduction reaction(ORR)activity,with high E_(1/2)(0.90 V)and E_(onset)(1.01 V)values.More importantly,Zn-H_(2)O_(2)batteries achieve a record-breaking peak-power density of 510 mW cm^(-2)and a high specific energy density of 953 Wh kg^(-1).展开更多
Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-...Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-supervised and reference-guided learning approach for depth inpainting.For the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information.This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map.Then,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual information.The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values.We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes.In summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information.Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.展开更多
基金The authors are highly thankful to the Development Research Center of Guangxi Relatively Sparse-populated Minorities(ID:GXRKJSZ201901)to the Natural Science Foundation of Guangxi Province(No.2018GXNSFAA281164)This research was financially supported by the project of outstanding thousand young teachers’training in higher education institutions of Guangxi,Guangxi Colleges and Universities Key Laboratory Breeding Base of System Control and Information Processing.
文摘Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have emerged.Compared to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single image.In addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual performance.In this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics.Then,we review the improved network structures and loss functions of GAN-based perceptual SISR.Subsequently,the advantages and disadvantages of different networks are analyzed by multiple comparative experiments.Finally,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.
基金supported by the National Natural Science Foundation of China(U1832136 and 21303038)the Intelligent Manufacturing Institute of Hefei University of Technology Project for Scientific and Technological Achievements+1 种基金the Fundamental Research Funds for the Central Universities(JZ2020HGQA0149,PA2022GDGP0029 and PA2023GDGP0042)the Anhui Natural Science Foundation Project(2308085ME140)。
文摘Aqueous metal-H_(2)O_(2)cells are emerging as power batteries because of their large theoretical energy densities and multiple application scenarios,especially in underwater environments.However,the peak power densities are less than 300 mW cm^(-2)for most reported metal-H_(2)O_(2)cells based on Mg/Al or their alloys due to the self-corrosion.Herein,we reported a Zn-H_(2)O_(2)cell with ultrafine bean-pod-like ZnCo/N-doped electrocatalysts that were synthesized via multifunctional single-cell-chain biomass.The electrocatalyst provides abundant active sites on the crinkly interface and offers a shortened pathway for electron/ion transfer due to the desired root-like carbon nanotube(CNT)arrays.Therefore,the optimized electrocatalyst exhibited outstanding oxygen reduction reaction(ORR)activity,with high E_(1/2)(0.90 V)and E_(onset)(1.01 V)values.More importantly,Zn-H_(2)O_(2)batteries achieve a record-breaking peak-power density of 510 mW cm^(-2)and a high specific energy density of 953 Wh kg^(-1).
基金partially supported by a grant from the National Natural Science Foundation of China(No.61922006).
文摘Depth information can benefit various computer vision tasks on both images and videos.However,depth maps may suffer from invalid values in many pixels,and also large holes.To improve such data,we propose a joint self-supervised and reference-guided learning approach for depth inpainting.For the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information.This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map.Then,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual information.The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values.We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes.In summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information.Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.
