Image to Image Translation Based on Differential Image Pix2Pix Model

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.

Pancreatic carcinoma coexisting with chronic pancreatitis versus tumor-forming pancreatitis:Diagnostic utility of the time-signal intensity curve from dynamic contrast-enhanced MR imaging

AIM: To evaluate the ability of the time-signal intensity curve (TIC) of the pancreas obtained from dynamic contrast-enhanced magnetic resonance imaging (MRI) for differentiation of focal pancreatic masses, especially pancreatic carcinoma coexisting with chronic pancreatitis and tumor-forming pancreatitis. METHODS: Forty-eight consecutive patients who underwent surgery for a focal pancreatic mass, including pancreatic ductal carcinoma (n = 33), tumor-forming pancreatitis (n = 8), and islet cell tumor (n = 7), were reviewed. Five pancreatic carcinomas coexisted with longstanding chronic pancreatitis. The pancreatic TICs were obtained from the pancreatic mass and the pancreatic parenchyma both proximal and distal to the mass lesion in each patient, prior to surgery, and were classified into 4 types according to the time to a peak: 25 s and 1, 2, and 3 min after the bolus injection of contrast material, namely, type-Ⅰ, Ⅱ, Ⅲ, and Ⅳ, respectively, and were then compared to the corresponding histological pancreatic conditions. RESULTS: Pancreatic carcinomas demonstrated type-Ⅲ (n = 13) or Ⅳ (n = 20) TIC. Tumor-forming pancreatitis showed type-Ⅱ (n = 5) or Ⅲ (n = 3) TIC. All islet cell tumors revealed type-Ⅰ. The type-Ⅳ TIC was only recognized in pancreatic carcinoma, and the TIC of carcinoma always depicted the slowest rise to a peak among the 3 pancreatic TICs measured in each patient, even in patients with chronic pancreatitis.CONCLUSION: Pancreatic TIC from dynamic MRI provides reliable information for distinguishing pancreatic carcinoma from other pancreatic masses, and may enable us to avoid unnecessary pancreatic surgery and delays in making a correct diagnosis of pancreatic carcinoma, especially, in patients with longstanding chronic pancreatitis.

Enhanced quantitative X-ray phase-contrast images using Foucault differential filters

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.

Fast and Simple Motion Tracking Unit with Motion Estimation

Smweillance system using active tracking camera has no distance limitation of surveillance range compared to supersonic or sound sensors. However, complex motion tracking algorithm requires huge amount of computation, and it often requires exfmasive DSPs or embedded processors. This paper proposes a novel motion tracking trait based on different image for fast and simple motion tracking. It uses configuration factor to avoid noise and inaccuracy. It reduces the required computation significantly, so as to be implemented on Field Programmable Gate Array（FFGAs ） instead of expensive Digital Signal Processing（DSPs）. It also performs calculation for motion estimation in video compression, so it can be easily combined with surveil system with video recording functionality based on video compression. The proposed motion tracking system implemented on Xilinx Vertex-4 FPGA can process 48 frames per second, and operating frequency of motion tracking trait is 100 MHz.

Denoising method of X-ray phase contrast DR image for TRISO-coated fuel particles

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.