Machine learning based liver disease diagnosis: A systematic review
The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
Deblurring via stochastic refinement
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input
image. However, most existing methods produce a deterministic estimate of the clean image …
image. However, most existing methods produce a deterministic estimate of the clean image …
Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better
We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …
Bibliometric analysis and review of deep learning-based crack detection literature published between 2010 and 2022
The use of deep learning (DL) in civil inspection, especially in crack detection, has
increased over the past years to ensure long-term structural safety and integrity. To achieve …
increased over the past years to ensure long-term structural safety and integrity. To achieve …
Deblurgan: Blind motion deblurring using conditional adversarial networks
O Kupyn, V Budzan, M Mykhailych… - Proceedings of the …, 2018 - openaccess.thecvf.com
We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …
Perceptual enhancement for autonomous vehicles: Restoring visually degraded images for context prediction via adversarial training
Realizing autonomous vehicles is one of the ultimate dreams for humans. However,
perceptual information collected by sensors in dynamic and complicated environments, in …
perceptual information collected by sensors in dynamic and complicated environments, in …
Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training
Blind non-uniform image deblurring for severe blurs induced by large motions is still
challenging. Multi-scale (MS) approach has been widely used for deblurring that …
challenging. Multi-scale (MS) approach has been widely used for deblurring that …
Effects of image degradation and degradation removal to CNN-based image classification
Just like many other topics in computer vision, image classification has achieved significant
progress recently by using deep learning neural networks, especially the Convolutional …
progress recently by using deep learning neural networks, especially the Convolutional …
Blind image quality assessment with active inference
Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea
to design BIQA methods by mimicking the working mechanism of human visual system …
to design BIQA methods by mimicking the working mechanism of human visual system …
Efficient dynamic scene deblurring using spatially variant deconvolution network with optical flow guided training
Y Yuan, W Su, D Ma - … of the IEEE/CVF conference on …, 2020 - openaccess.thecvf.com
In order to remove the non-uniform blur of images captured from dynamic scenes, many
deep learning based methods design deep networks for large receptive fields and strong …
deep learning based methods design deep networks for large receptive fields and strong …