Review of deep learning algorithms and architectures
A Shrestha, A Mahmood - IEEE access, 2019 - ieeexplore.ieee.org
Deep learning (DL) is playing an increasingly important role in our lives. It has already made
a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars …
a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars …
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Deep image deblurring: A survey
Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …
sharp image from a blurred input image. Advances in deep learning have led to significant …
On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Learning deep CNN denoiser prior for image restoration
Abstract Model-based optimization methods and discriminative learning methods have been
the two dominant strategies for solving various inverse problems in low-level vision …
the two dominant strategies for solving various inverse problems in low-level vision …
Using deep neural networks for inverse problems in imaging: beyond analytical methods
Traditionally, analytical methods have been used to solve imaging problems such as image
restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and …
restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and …
Dehazenet: An end-to-end system for single image haze removal
Single image haze removal is a challenging ill-posed problem. Existing methods use
various constraints/priors to get plausible dehazing solutions. The key to achieve haze …
various constraints/priors to get plausible dehazing solutions. The key to achieve haze …
Dynamic scene deblurring using spatially variant recurrent neural networks
Due to the spatially variant blur caused by camera shake and object motions under different
scene depths, deblurring images captured from dynamic scenes is challenging. Although …
scene depths, deblurring images captured from dynamic scenes is challenging. Although …
A survey of deep learning approaches to image restoration
In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …
Image super-resolution using deep convolutional networks
We propose a deep learning method for single image super-resolution (SR). Our method
directly learns an end-to-end mapping between the low/high-resolution images. The …
directly learns an end-to-end mapping between the low/high-resolution images. The …