Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Intelligent Sensing and Artificial Intelligence for Image Processing
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Sensing and Artificial Intelligence for Image Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 2156

Special Issue Editors

Prof. Dr. Hui-liang Shen

E-Mail Website
Guest Editor
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: multispectral imaging; image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Eryun Liu

E-Mail Website
Guest Editor
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: biometrics; image processing; computer vision; collaborative perception
Dr. Siyuan Cao

E-Mail Website
Guest Editor
Ningbo Research Institute, Zhejiang University, Ningbo 315100, China
Interests: image registration; image denoising; image fusion; object detection and tracking; deep learning

Special Issue Information

Dear Colleagues, 

Due to advances in both intelligent sensing and artificial intelligence, novel theories and methods have been proposed with regard to image processing. This transformation has led to intelligent sensing and artificial intelligence being employed to solve various problems, including, but not limited to, object detection, recognition, classification, segmentation, and tracking. The application of intelligent sensing and artificial intelligence in image processing has moved beyond traditional fields and now covers a wide range of fields such as healthcare, surveillance, autonomous vehicles, agriculture and industrial automation, offering more solutions to complex challenges. 

The Special Issue aims to present recent advanced techniques in image processing based on artificial intelligence and intelligent sensing. Topics may include, but are not limited to, the following: 

  • Image fusion
  • Image registration
  • Image segmentation
  • Image recovery and enhancement
  • Image classification
  • Pattern recognition
  • Remote sensing image processing
  • Object detection, tracking and recognition
  • Action recognition
  • Neural networks and deep learning

Prof. Dr. Hui-liang Shen
Dr. Eryun Liu
Dr. Siyuan Cao
Guest Editors

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. Sensors 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 2600 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 fusion
  • image registration
  • image segmentation
  • image recovery and enhancement
  • image classification
  • pattern recognition
  • remote sensing image processing
  • object detection, tracking and recognition
  • action recognition
  • neural networks and deep learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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

Research

15 pages, 11202 KiB  
Article
Deep Recyclable Trash Sorting Using Integrated Parallel Attention
by Hualing Lin, Xue Zhang, Junchen Yu, Ji Xiang and Hui-Liang Shen
Sensors 2024, 24(19), 6434; https://doi.org/10.3390/s24196434 - 4 Oct 2024
Viewed by 616
Abstract
Sorting recyclable trash is critical to reducing energy consumption and mitigating environmental pollution. Currently, trash sorting heavily relies on manpower. Computer vision technology enables automated trash sorting. However, existing trash image classification datasets contain a large number of images without backgrounds. Moreover, the [...] Read more.
Sorting recyclable trash is critical to reducing energy consumption and mitigating environmental pollution. Currently, trash sorting heavily relies on manpower. Computer vision technology enables automated trash sorting. However, existing trash image classification datasets contain a large number of images without backgrounds. Moreover, the models are vulnerable to background interference when categorizing images with complex backgrounds. In this work, we provide a recyclable trash dataset that supports model training and design a model specifically for trash sorting. Firstly, we introduce the TrashIVL dataset, an image dataset for recyclable trash sorting encompassing five classes (TrashIVL-5). All images are collected from public trash datasets, and the original images were captured by RGB imaging sensors, containing trash items with real-life backgrounds. To achieve refined recycling and improve sorting efficiency, the TrashIVL dataset can be further categorized into 12 classes (TrashIVL-12). Secondly, we propose the integrated parallel attention module (IPAM). Considering the susceptibility of sensor-based systems to background interference in real-world trash sorting scenarios, our IPAM is specifically designed to focus on the essential features of trash images from both channel and spatial perspectives. It can be inserted into convolutional neural networks (CNNs) as a plug-and-play module. We have constructed a recyclable trash sorting network building upon the IPAM, which produces an acuracy of 97.42% on TrashIVL-5 and 94.08% on TrashIVL-12. Our work is an effective attempt of computer vision in recyclable trash sorting. It makes a positive contribution to environmental protection and sustainable development. Full article
(This article belongs to the Special Issue Intelligent Sensing and Artificial Intelligence for Image Processing)
►▼ Show Figures

Figure 1

18 pages, 22790 KiB  
Article
Universal Image Restoration with Text Prompt Diffusion
by Bing Yu, Zhenghui Fan, Xue Xiang, Jiahui Chen and Dongjin Huang
Sensors 2024, 24(12), 3917; https://doi.org/10.3390/s24123917 - 17 Jun 2024
Viewed by 1069
Abstract
Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both learning-based and prior-based approaches, heavily rely on low-quality image features. However, it is challenging to extract degradation information from diverse low-quality [...] Read more.
Universal image restoration (UIR) aims to accurately restore images with a variety of unknown degradation types and levels. Existing methods, including both learning-based and prior-based approaches, heavily rely on low-quality image features. However, it is challenging to extract degradation information from diverse low-quality images, which limits model performance. Furthermore, UIR necessitates the recovery of images with diverse and complex types of degradation. Inaccurate estimations further decrease restoration performance, resulting in suboptimal recovery outcomes. To enhance UIR performance, a viable approach is to introduce additional priors. The current UIR methods have problems such as poor enhancement effect and low universality. To address this issue, we propose an effective framework based on a diffusion model (DM) for universal image restoration, dubbed ETDiffIR. Inspired by the remarkable performance of text prompts in the field of image generation, we employ text prompts to improve the restoration of degraded images. This framework utilizes a text prompt corresponding to the low-quality image to assist the diffusion model in restoring the image. Specifically, a novel text–image fusion block is proposed by combining the CLIP text encoder and the DA-CLIP image controller, which integrates text prompt encoding and degradation type encoding into time step encoding. Moreover, to reduce the computational cost of the denoising UNet in the diffusion model, we develop an efficient restoration U-shaped network (ERUNet) to achieve favorable noise prediction performance via depthwise convolution and pointwise convolution. We evaluate the proposed method on image dehazing, deraining, and denoising tasks. The experimental results indicate the superiority of our proposed algorithm. Full article
(This article belongs to the Special Issue Intelligent Sensing and Artificial Intelligence for 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 1-2
Back to TopTop