Single Image Super-Resolution via Deep Dense Network

University of Technology Sydney. Faculty of Engineering and Information Technology.Image Super-Resolution (SR) is a research field of computer vision, which enhances the resolution of an imaging system. The need for high resolution is common in computer vision applications for better performance in pattern recognition and analysis of images. However, recovering of the HR image from LR image is a highly ill-posed problem. In this thesis, the image SR problem is solved from three aspects with deep dense network models, including improving reconstruction accuracy, optimizing model training-time memory consumption, and extending effective SR scale ranges. Chapter 1 introduces the importance of image SR reconstruction and summarizes the challenges of image SR problem. Chapter 2 reviews the existing image SR methods, analyses their limitations and explains some related fundamental theories. Chapter 3 proposes a bi-dense model to improve image SR performance based on the dense connections for feature reuse. The bi-dense network does not only reuse local feature layers in the dense block, but also reuses the block information in the network to archive excellent performance with a moderate number of parameters. Chapter 4 evaluates the memory consumption of the vanilla dense model for image SR. For solving this problem, we introduce shared memory strategy into image SR by proposing a memory-optimized deep dense network. Chapter 5 discovers most of the deep SR methods are inefficient or impractical for generating SR of any scale factor, and proposes a novel Any-Scale Deep Network (ASDN), which requires few training scales to achieve one unified network for any-scale SR. In order to design such a powerful network architecture, we propose Laplacian Frequency Representation to predict SR results of the small ratio range and Recursive Deployment for SR of any larger scale. In this way, the required training data and update periods are substantially decreased to optimize the any-scale SR network. All these algorithms are aimed to solve the single image SR problem. These algorithms are tested on many public datasets and the results on those datasets demonstrate superior performance of our approach over the state-of-the-art methods and validate the effectiveness and correctness of our methods

ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution

Deep convolutional neural networks have significantly improved the peak signal-to-noise ratio of SuperResolution (SR). However, image viewer applications commonly allow users to zoom the images to arbitrary magnification scales, thus far imposing a large number of required training scales at a tremendous computational cost. To obtain a more computationally efficient model for arbitrary scale SR, this paper employs a Laplacian pyramid method to reconstruct any-scale high-resolution (HR) images using the high-frequency image details in a Laplacian Frequency Representation. For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels. SR of larger scales is computed by recursion from small scales, which significantly reduces the computational cost. For a full comparison, fixed- and any-scale experiments are conducted using various benchmarks. At fixed scales, ASDN outperforms predefined upsampling methods (e.g., SRCNN, VDSR, DRRN) by about 1 dB in PSNR. At any-scale, ASDN generally exceeds Meta-SR on many scales

Do the combination of multiparametric MRI-based radiomics and selected blood inflammatory markers predict the grade and proliferation in glioma patients?

PURPOSEWe aimed to explore whether multiparametric magnetic resonance imaging (MRI)-based radiomics combined with selected blood inflammatory markers could effectively predict the grade and proliferation in glioma patients.METHODSThis retrospective study included 152 patients histopathologically diagnosed with glioma. Stratified sampling was used to divide all patients into a training cohort (n=107) and a validation cohort (n=45) according to a ratio of 7:3, and five-fold repeat cross-validation was adopted in the training cohort. Multiparametric MRI and clinical parameters, including age, the neutrophil-lymphocyte ratio and red cell distribution width, were assessed. During image processing, image registration and gray normalization were conducted. A radiomics analysis was performed by extracting 1584 multiparametric MRI-based features, and the least absolute shrinkage and selection operator (LASSO) was applied to generate a radiomics signature for predicting grade and Ki-67 index in both training and validation cohorts. Statistical analysis included analysis of variance, Pearson correlation, intraclass correlation coefficient, multivariate logistic regression, Hosmer-Lemeshow test, and receiver operating characteristic (ROC) curve.RESULTSThe radiomics signature demonstrated good performance in both the training and validation cohorts, with areas under the ROC curve (AUCs) of 0.92, 0.91, and 0.94 and 0.94, 0.75, and 0.82 for differentiating between low and high grade gliomas, grade III and grade IV gliomas, and low Ki-67 and high Ki-67, respectively, and was better than the clinical model; the AUCs of the combined model were 0.93, 0.91, and 0.95 and 0.94, 0.76, and 0.80, respectively.CONCLUSIONBoth the radiomics signature and combined model showed high diagnostic efficacy and outperformed the clinical model. The clinical factors did not provide additional improvement in the prediction of the grade and proliferation index in glioma patients, but the stability was improved

Acute Colonic Pseudo-Obstruction with Feeding Intolerance in Critically Ill Patients: A Study according to Gut Wall Analysis

Objective. To compare the differences between acute colonic pseudo-obstruction (ACPO) with and without acute gut wall thickening. Methods. ACPO patients with feeding tolerance were divided into ACPO with no obvious gut wall thickening (ACPO-NT) group and ACPO with obvious acute gut wall thickening (ACPO-T) group according to computed tomography and abdominal radiographs. Patients’ condition, responses to supportive measures, pharmacologic therapy, endoscopic decompression, and surgeries and outcomes were compared. Results. Patients in ACPO-T group had a significantly higher APACHE II (11.82 versus 8.25, p=0.008) and SOFA scores (6.47 versus 3.54, p<0.001) and a significantly higher 28-day mortality (17.78% versus 4.16%, p=0.032) and longer intensive care unit stage (4 versus 16 d, p<0.001). Patients in ACPO-NT group were more likely to be responsive to supportive treatment (62.50% versus 24.44%, p<0.001), neostigmine (77.78% versus 17.64%, p<0.001), and colonoscopic decompression (75% versus 42.86%, p=0.318) than those in ACPO-T group. Of the patients who underwent ileostomy, 81.25% gained benefits. Conclusions. ACPO patients with gut wall thickening are more severe and are less likely to be responsive to nonsurgical treatment. Ileostomy may be a good option for ACPO patients with gut wall thickening who are irresponsive to nonsurgical treatment