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Algorithms
Special Issues
Machine Learning and Deep Learning in Image Processing
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Machine Learning and Deep Learning in Image Processing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 1932

Special Issue Editor

Dr. Yi Chang

E-Mail Website
Guest Editor
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: image enhancement; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image processing has received great attention in recent decades with the rapid development of image sensors. The goal of image processing is to manipulate the images, such as image enhancement, analysis, restoration, reconstruction, compression, and so on, by using different algorithms. It is of great importance to perform the pre-processing to improve the image quality before further application.

The conventional wavelet-based digital filtering technique method have been widely investigated. Unfortunately, the hand-crafted methods have limited representative ability for complex scenes. In recent decades, the machine learning technique’s sparse representation, low-rank matrix/tensor recovery, and deep convolutional neural network have made tremendous progress in which they have profoundly changed the paradigm of research from a data-driven perspective.

This Special Issue aims to investigate the use of machine learning and deep learning techniques to advance the improvement of image processing, such as image denoising, deblurring, super-resolution dehazing, deraining, and so on. We would like to invite researchers to submit papers on the topic, from all viewpoints, including theoretical issues, algorithms, systems, and industrial applications

Dr. Yi Chang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • image restoration
  • image analysis
  • deep learning

Published Papers (1 paper)

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Research

24 pages, 4984 KiB  
Article
Detecting Image Forgery over Social Media Using U-NET with Grasshopper Optimization
by Niousha Ghannad and Kalpdrum Passi
Algorithms 2023, 16(9), 399; https://doi.org/10.3390/a16090399 - 23 Aug 2023
Viewed by 1439
Abstract
Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger [...] Read more.
Currently, video and digital images possess extensive utility, ranging from recreational and social media purposes to verification, military operations, legal proceedings, and penalization. The enhancement mechanisms of this medium have undergone significant advancements, rendering them more accessible and widely available to a larger population. Consequently, this has facilitated the ease with which counterfeiters can manipulate images. Convolutional neural network (CNN)-based feature extraction and detection techniques were used to carry out this task, which aims to identify the variations in image features between modified and non-manipulated areas. However, the effectiveness of the existing detection methods could be more efficient. The contributions of this paper include the introduction of a segmentation method to identify the forgery region in images with the U-Net model’s improved structure. The suggested model connects the encoder and decoder pipeline by improving the convolution module and increasing the set of weights in the U-Net contraction and expansion path. In addition, the parameters of the U-Net network are optimized by using the grasshopper optimization algorithm (GOA). Experiments were carried out on the publicly accessible image tempering detection evaluation dataset from the Chinese Academy of Sciences Institute of Automation (CASIA) to assess the efficacy of the suggested strategy. The results show that the U-Net modifications significantly improve the overall segmentation results compared to other models. The effectiveness of this method was evaluated on CASIA, and the quantitative results obtained based on accuracy, precision, recall, and the F1 score demonstrate the superiority of the U-Net modifications over other models. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image Processing)
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