Computer Vision News - November 2020

Research 10 In this paper, a semi-supervised deep learning network approach recovers with accuracy high-resolution (HR) X-ray Computed Tomography (CT) images from low-resolution (LR) counterparts. Deep convolutional neural network (CNN), residual learning, and network in network techniques are applied and further explored in the article review. You probably remember GANs from previous articles and they happen to be the main building block of this proposal. A nonlinear end-to-end mapping from the noisy LR to denoised/deblurred HR outputs is performed. Famously, 1x1 CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) Hello everyone and welcome to this month’s presentation of the paper titled “CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)” with the main authors Chenyu You and Guang Li. by Ioannis Valasakis (@wizofe) convolutions were used in Google’s Inception V3 model, mapping the input data into 3-4 separate spaces that are smaller than the original space, i.e. reducing the dimensionality in the filter dimension. Larger convolutions (3x3 or 5x5) are used consequently to map all the corelations between those and the smaller 3D spaces. Similarly, in this paper 1x1 CNN used to compress the output of the hidden layer and optimise the number of layers and filter for each convolutional layer. One of the important future consequences would be the usage of HRCT to explore radiomics features, which can be very useful for screening and diagnosis of various diseases. HRCT can also be produced with Notably, in this paper and after clinical evaluation the proposed solution provided the best image sharpness, contrast resolution, diagnostic acceptability, and overall image quality, which is notable. It required though more training than typical GANs (although not specified) and the Wasserstein distance may yield biased sample gradients.