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Viewing 1-4 of 4 total results
Deep Iterative Residual Convolutional Network for Single ...
The goal of the single image super-resolution (SISR) is to recover the high-resolution (HR) image from its low-resolution (LR) counterpart. SISR problem is a fundamental low-level vision and image processing problem with various practical applications in satellite imaging, medical imaging, astronomy, microscopy, seismology, remote sensing, surveillance, biometric, image compression, etc....
OSA | End-to-end Res-Unet based reconstruction algorithm ...
Recently, deep neural networks have attracted great attention in photoacoustic imaging (PAI). In PAI, reconstructing the initial pressure distribution from acquired photoacoustic (PA) signals is a typically inverse problem. In this paper, an end-to-end Unet with residual blocks (Res-Unet) is designed and trained to solve the inverse problem in PAI. The performance of the proposed algorithm is ...
Facial image super-resolution guided by adaptive geometric ...
This paper addresses the traditional issue of restoring a high-resolution (HR) facial image from a low-resolution (LR) counterpart. Current state-of-the-art super-resolution (SR) methods commonly adopt the convolutional neural networks to learn a non-linear complex mapping between paired LR and HR images. They discriminate local patterns expressed by the neighboring pixels along the planar ......
A deep convolutional neural network using directional ...
For the last few years, researchers have had great successes from deep networks in many low‐level computer vision applications, such as denoising 21-23 and super‐resolution applications. 24, 25 Inspired by the success of the deep convolutional neural network, we propose a novel low‐dose CT denoising framework designed to detect and remove ......
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