(1) is firstly explained.y=Hx+nwhere x and y are the HR depth ground truth and the LR depth observation respectively, H represents the down-sampling matrix, n is the random noise. Obviously, the depth map enhancement is to infer x by given y which is a highly ill-posed problem. ...
Light-field image super-resolution 11054 using convolutional neural network. IEEE Signal Processing Letters, 24(6):848–852, 2017. [28] Jingyi Yu, Xu Hong, Jason Yang, and Yi Ma. Dgene: The light of science, the light of future. http://www. plex-vr.com/product/mode...
These are explained in detail in Subsection 3.3. 3.2 Network architecture Figure 2 shows the architecture and parameters of the translator network, which is named as cascaded- residual adversarial network, abbreviated CRAN. It follows the main structure of U-Net [18] and Pix2Pix [1] with ...
The major difference from the 2D model is the data preprocessing part; it will be explained in detail in subsection 3.1. The IResUnet3+ 3D model diagram is shown in Fig. 6 below. Fig. 6 Improved U-Net3+ with stage residual (IResUnet3+) 3D model Full size image 3D data ...
deviation of 0.5. More details about preprocessing are explained furthermore intheir paper. Other details, such as the fact that the classification output is sampled only at the last timestep for the training of the neural network, can be found in their preprocessing script that we adapted in ...
In addition to that, the simulation result was showed that the predictions of breakthrough curve model has been coincided well with the measured values which explained that the depth 25 cm with grain size 1.5 mm of GAC filter bed will be give the optimum removal of residual chlorine from ...
The details of each improvement are explained in the following sections. Figure 1 illustrates the overall structure of the model. The proposed model has a four-layer encoder-decoder structure. In the encoder, RGCM is used for image downsampling with 64 filters in the first downsampling module ...
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Since averaging across voxels removes independent noise among voxels, this residual reflects a component of the brain signal that is not explained by the stimulus, for example, top-down attention is not modeled in the GLM. To determine the correlation in the time-series of residuals between ROIs...
2.3.2. SENet combined with attention residual learning In this work, we suggest a SENet incorporating the attention residual learning method, which is explained as follows: The standard procedure in SENet is to first perform global average pooling in the spatial dimension (which is called Squeeze)...