Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Image Fusion and Registration for High-Resolution Image Processing
share announcement

Image Fusion and Registration for High-Resolution Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 2576

Special Issue Editor

Prof. Dr. Gemma Piella

E-Mail Website
Guest Editor
Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08108 Barcelona, Spain
Interests: medical image analysis; machine learning; data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image registration and fusion algorithms are essential for processing techniques in applications that require integration of information, such as medical image analysis, remote sensing, or surveillance. In these applications, high-resolution, multi-temporal, and multi-sensor data are often used. This brings new challenges, since most of the traditional registration and fusion algorithms fail when the resolution increases significantly. The high dimensionality of the acquired data increases the complexity of processing, and the design of adequate algorithms and architectures is required.

The aim of this Special Issue of Electronics is to present state-of-the-art investigations related to image registration and fusion for high-resolution processing. The topics of interest include, but are not limited to:

  • Novel fusion and registration algorithms for high-resolution images
  • Advanced information processing and architectures for high-resolution images
  • Image registration and fusion for super resolution
  • Resolution-enhanced image fusion and registration methods
  • High-quality reviews of fusion and registration algorithms for high-resolution images

Prof. Dr. Gemma Piella
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 3015 KiB  
Article
A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution
by Lei Zha, Yu Yang, Zicheng Lai, Ziwei Zhang and Juan Wen
Electronics 2021, 10(11), 1234; https://doi.org/10.3390/electronics10111234 - 22 May 2021
Cited by 7 | Viewed by 1861
Abstract
In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote [...] Read more.
In recent years, neural networks for single image super-resolution (SISR) have applied more profound and deeper network structures to extract extra image details, which brings difficulties in model training. To deal with deep model training problems, researchers utilize dense skip connections to promote the model’s feature representation ability by reusing deep features of different receptive fields. Benefiting from the dense connection block, SRDensenet has achieved excellent performance in SISR. Despite the fact that the dense connected structure can provide rich information, it will also introduce redundant and useless information. To tackle this problem, in this paper, we propose a Lightweight Dense Connected Approach with Attention for Single Image Super-Resolution (LDCASR), which employs the attention mechanism to extract useful information in channel dimension. Particularly, we propose the recursive dense group (RDG), consisting of Dense Attention Blocks (DABs), which can obtain more significant representations by extracting deep features with the aid of both dense connections and the attention module, making our whole network attach importance to learning more advanced feature information. Additionally, we introduce the group convolution in DABs, which can reduce the number of parameters to 0.6 M. Extensive experiments on benchmark datasets demonstrate the superiority of our proposed method over five chosen SISR methods. Full article
(This article belongs to the Special Issue Image Fusion and Registration for High-Resolution Image Processing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop