Mathematical Problems in Engineering

Machine Learning and its Applications in Image Restoration


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
23 Oct 2020

Lead Editor

1Guangxi University, Nanning, China

2Nanjing University of Information Science and Technology, Nanjing, China

3Colorado State University, Fort Collins, USA

4Changsha University of Science and Technology, Changsha, China

5Guilin University of Electronic Technology, Guilin, China

This issue is now closed for submissions.
More articles will be published in the near future.

Machine Learning and its Applications in Image Restoration

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Machine learning is a new topic of increasing interest in scientific research, involving many practical engineering applications besides mathematical analysis of algorithms. It is known that many common optimisation problems exist in machine learning, such as algorithm choices, and model solutions. Although there have been notable achievements in the fields of machine learning and optimisation, many challenging problems remain, such as large-scale optimisation problems, fast algorithms for machine learning, and applications of machine learning to image denoising, which is a hard-to-solve inverse problem.

Images are often corrupted by impulse noise, one of the most common types of noise. Impulse noise has a short duration but degrades the quality of images. Impulse noise has two common types: salt-and-pepper noise and random-valued noise. There exist many established methods to remove impulse noise while preserving the integrity of edges and image details. Among these methods, two have attracted intensive attention from researchers. One is the median filter method (along with some important variants) – this method restores the noise pixels poorly when the noise ratio is high, and the recovered image may lose its details and be distorted. The other method is the variational approach, which may change the grey level of every pixel, including the uncorrupted ones.

The aim of this Special Issue is to encourage contributions of original research articles as well as review articles on new developments in machine learning, novel optimisation algorithms, and their applications to image restoration.

Potential topics include but are not limited to the following:

  • Machine learning, cloud computing, and their applications in image restoration
  • Large-scale deep model, black box machine learning algorithms
  • Nonlinear eigenvalue problems, tensor analysis, and their applications in compressive sensing
  • Nonlinear analysis for image restoration and machine learning
  • Training data, data analysis, and complexity in machine learning

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 8873507
  • - Research Article

Convergence Analysis on an Accelerated Proximal Point Algorithm for Linearly Constrained Optimization Problems

Sha Lu | Zengxin Wei
  • Special Issue
  • - Volume 2020
  • - Article ID 8869132
  • - Research Article

Static Game Models and Applications Based on Market Supervision and Compliance Management of P2P Platform

Sulin Pang | Junkun Yang | ... | Jun Cao
  • Special Issue
  • - Volume 2020
  • - Article ID 7859286
  • - Research Article

Modified Three-Term Liu–Storey Conjugate Gradient Method for Solving Unconstrained Optimization Problems and Image Restoration Problems

Yulun Wu | Mengxiang Zhang | Yan Li
  • Special Issue
  • - Volume 2020
  • - Article ID 7945467
  • - Research Article

An Accelerated Conjugate Gradient Algorithm for Solving Nonlinear Monotone Equations and Image Restoration Problems

Haishan Feng | Tingting Li
  • Special Issue
  • - Volume 2020
  • - Article ID 6157294
  • - Research Article

A Descent Conjugate Gradient Algorithm for Optimization Problems and Its Applications in Image Restoration and Compression Sensing

Junyue Cao | Jinzhao Wu
  • Special Issue
  • - Volume 2020
  • - Article ID 6391321
  • - Research Article

A New Hybrid PRPFR Conjugate Gradient Method for Solving Nonlinear Monotone Equations and Image Restoration Problems

Yingjie Zhou | Yulun Wu | Xiangrong Li
  • Special Issue
  • - Volume 2020
  • - Article ID 6279543
  • - Research Article

A Modified Dai–Liao Conjugate Gradient Method with a New Parameter for Solving Image Restoration Problems

Junyu Lu | Yong Li | Hongtruong Pham
  • Special Issue
  • - Volume 2020
  • - Article ID 5472351
  • - Research Article

Complexity Induced by External Stimulations in a Neural Network System with Time Delay

Bin Zhen | Dingyi Zhang | Zigen Song
  • Special Issue
  • - Volume 2020
  • - Article ID 4381515
  • - Research Article

A Modified Three-Term Type CD Conjugate Gradient Algorithm for Unconstrained Optimization Problems

Zhan Wang | Pengyuan Li | ... | Hongtruong Pham
  • Special Issue
  • - Volume 2020
  • - Article ID 6210965
  • - Research Article

A Modified Nonlinear Conjugate Gradient Method with the Armijo Line Search and Its Application

Mengxiang Zhang | Yingjie Zhou | Songhua Wang
Mathematical Problems in Engineering
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