Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review
In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …
[HTML][HTML] Medical image super-resolution for smart healthcare applications: A comprehensive survey
The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …
models and the Internet of Things (IoT), is creating unprecedented opportunities for …
Deep learning for image super-resolution: A survey
Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …
enhance the resolution of images and videos in computer vision. Recent years have …
Generative adversarial networks (GANs) challenges, solutions, and future directions
Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …
has recently gained significant attention. GANs learn complex and high-dimensional …
Video enhancement with task-oriented flow
Many video enhancement algorithms rely on optical flow to register frames in a video
sequence. Precise flow estimation is however intractable; and optical flow itself is often a …
sequence. Precise flow estimation is however intractable; and optical flow itself is often a …
A review of the deep learning methods for medical images super resolution problems
Y Li, B Sixou, F Peyrin - Irbm, 2021 - Elsevier
Super resolution problems are widely discussed in medical imaging. Spatial resolution of
medical images are not sufficient due to the constraints such as image acquisition time, low …
medical images are not sufficient due to the constraints such as image acquisition time, low …
Photo-realistic single image super-resolution using a generative adversarial network
Despite the breakthroughs in accuracy and speed of single image super-resolution using
faster and deeper convolutional neural networks, one central problem remains largely …
faster and deeper convolutional neural networks, one central problem remains largely …
[HTML][HTML] A full data augmentation pipeline for small object detection based on generative adversarial networks
Object detection accuracy on small objects, ie, objects under 32× 32 pixels, lags behind that
of large ones. To address this issue, innovative architectures have been designed and new …
of large ones. To address this issue, innovative architectures have been designed and new …
Frame-recurrent video super-resolution
Recent advances in video super-resolution have shown that convolutional neural networks
combined with motion compensation are able to merge information from multiple low …
combined with motion compensation are able to merge information from multiple low …
Enhancenet: Single image super-resolution through automated texture synthesis
Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
low-resolution input. Traditionally, the performance of algorithms for this task is measured …