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1. Abstract The objective of image fusion, is to make use of the complementary information in multiple images, to achieve a higher resolution and intelligibility. The fused images provide more… (More)
We study the relationship between response time (RT) variability and functional connectivity (FC) estimated by wavelet transform coherence, when performing a visual oddball task. A statistically… (More)
We present the first investigation of how the cortical regions of the brain respond to the sensations related to oral irritation, using functional near-infrared spectroscopy (fNIRS). fNIRS is used to… (More)
We develop an image despeckling method that combines nonlocal self-similarity filters with machine learning, which makes use of convolutional neural network (CNN) denoisers. It consists of three… (More)
This paper presents a comparative study of image fusion of MRI and CT images using various wavelet transforms. The fusion of the images is done by implementing a multi-resolution decomposition method… (More)
We propose a generative network based on high-dimensional convolution (HDC) layer for light field (LF) spatial and angular super-resolution (SR). Experiments are conducted on both synthetic and… (More)
We explore the benefits of perceptual loss for light field (LF) spatial recon struction in a high-dimensional convolutional neural network. The results outperform some state-of-the-art methods for LF… (More)
We describe a convolutional neural network for image despeckling with the exponential linear unit activation function, which outperforms state-of-the-art approaches on the reduction of speckle noise.