Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural netwo...Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.展开更多
3D-aware image synthesis has attained high quality and robust 3D consistency.Existing 3D controllable generative models are designed to synthesize 3D-aware images through a single modality,such as 2D segmentation or s...3D-aware image synthesis has attained high quality and robust 3D consistency.Existing 3D controllable generative models are designed to synthesize 3D-aware images through a single modality,such as 2D segmentation or sketches,but lack the ability to finely control generated content,such as texture and age.In pursuit of enhancing user-guided controllability,we propose Multi3D,a 3D-aware controllable image synthesis model that supports multi-modal input.Our model can govern the geometry of the generated image using a 2D label map,such as a segmentation or sketch map,while concurrently regulating the appearance of the generated image through a textual description.To demonstrate the effectiveness of our method,we have conducted experiments on multiple datasets,including CelebAMask-HQ,AFHQ-cat,and shapenet-car.Qualitative and quantitative evaluations show that our method outperforms existing state-of-the-art methods.展开更多
基金Subjects funded by the National Natural Science Foundation of China(Nos.62275216 and 61775181)the Natural Science Basic Research Programme of Shaanxi Province-Major Basic Research Special Project(Nos.S2018-ZC-TD-0061 and TZ0393)the Special Project for the Development of National Key Scientific Instruments and Equipment No.(51927804).
文摘Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.
基金supported by the National Science and Technology Major Project(Grant No.2021ZD0112902)the National Natural Science Foundation of China(Project No.62220106003)a Research Grant from Beijing Higher Institution Engineering Research Center,and Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology.
文摘3D-aware image synthesis has attained high quality and robust 3D consistency.Existing 3D controllable generative models are designed to synthesize 3D-aware images through a single modality,such as 2D segmentation or sketches,but lack the ability to finely control generated content,such as texture and age.In pursuit of enhancing user-guided controllability,we propose Multi3D,a 3D-aware controllable image synthesis model that supports multi-modal input.Our model can govern the geometry of the generated image using a 2D label map,such as a segmentation or sketch map,while concurrently regulating the appearance of the generated image through a textual description.To demonstrate the effectiveness of our method,we have conducted experiments on multiple datasets,including CelebAMask-HQ,AFHQ-cat,and shapenet-car.Qualitative and quantitative evaluations show that our method outperforms existing state-of-the-art methods.
