In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image gener...In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images.展开更多
TRISO (tristructural-isotropic) fuel is a type of micro fuel particles used in high-temperature gas-cooled reactors (HTGRs). Among the quality evaluation methods for such particles, inqine phase contrast imaging t...TRISO (tristructural-isotropic) fuel is a type of micro fuel particles used in high-temperature gas-cooled reactors (HTGRs). Among the quality evaluation methods for such particles, inqine phase contrast imaging technique (PCI) is more feasible for nondestructive measurement. Due to imaging hardware limitations, high noise level is a distinct feature of PCI images, and as a result, the dimensional measurement accuracy of TRISO-coated fuel particles decreases. Therefore, we propose an improved denoising hybrid model named as NL P-M model which introduces non-local theory and retains the merits of the Perona-Malik (P-M) model. The improved model is applied to numerical simulation and practical PCI images. Quanti- tative analysis proves that this new anisotropic diffusion model can preserve edge or texture information effectively, while ruling out noise and distinctly decreasing staircasing artifacts. Especially during the process of coating layer thickness measurement, the NL P-M model makes it easy to obtain continuous contours without noisy points or fake contour segments, thus enhancing the measurement accuracy. To address calculation complexity, a graphic processing unit (GPU) is adopted to realize the acceleration of the NL P-M denoising.展开更多
Several X-ray phase visualization methods are being real- ized for imaging of phase objects, such as biological and polymeric specimens. Grating-based phase-contrast imaging using a source-grating-attached X-ray tube ...Several X-ray phase visualization methods are being real- ized for imaging of phase objects, such as biological and polymeric specimens. Grating-based phase-contrast imaging using a source-grating-attached X-ray tube that provides partially coherent X rays is one of the most successful methods in this field.展开更多
基金supported in part by the Xinjiang Natural Science Foundation of China(2021D01C078).
文摘In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants 11275019,21106158 and 61077011in part by the National State Key Laboratory of Multiphase Complex Systems under Grant MPCS-2011-D-03+4 种基金in part by the National Key Technology R&D Program of China under Grant 2011 BAI02B02supported in part by the National Research Foundation of Korea(NRF)grantfunded by the Korean government(MEST)(No.2011-0020024)in part by the R&D program of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korean government Ministry of Knowledge Economy(No.20101020300730)the Defense Acquisition Program Administration and the Agency for Defense Development for the financial support provided by both institutions
文摘TRISO (tristructural-isotropic) fuel is a type of micro fuel particles used in high-temperature gas-cooled reactors (HTGRs). Among the quality evaluation methods for such particles, inqine phase contrast imaging technique (PCI) is more feasible for nondestructive measurement. Due to imaging hardware limitations, high noise level is a distinct feature of PCI images, and as a result, the dimensional measurement accuracy of TRISO-coated fuel particles decreases. Therefore, we propose an improved denoising hybrid model named as NL P-M model which introduces non-local theory and retains the merits of the Perona-Malik (P-M) model. The improved model is applied to numerical simulation and practical PCI images. Quanti- tative analysis proves that this new anisotropic diffusion model can preserve edge or texture information effectively, while ruling out noise and distinctly decreasing staircasing artifacts. Especially during the process of coating layer thickness measurement, the NL P-M model makes it easy to obtain continuous contours without noisy points or fake contour segments, thus enhancing the measurement accuracy. To address calculation complexity, a graphic processing unit (GPU) is adopted to realize the acceleration of the NL P-M denoising.
基金supported by the research fund of Dankook University(No.R000122495)
文摘Several X-ray phase visualization methods are being real- ized for imaging of phase objects, such as biological and polymeric specimens. Grating-based phase-contrast imaging using a source-grating-attached X-ray tube that provides partially coherent X rays is one of the most successful methods in this field.