Submit to Electronics Review for Electronics Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Image Processing Based on Convolution Neural Network

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (8 January 2025) | Viewed by 17796

Share This Special Issue

Special Issue Editors

Prof. Dr. Shaozhang Niu

E-Mail Website
Guest Editor

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: multimedia security; image recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Jiwei Zhang

E-Mail Website
Guest Editor

School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: computer vision; information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of Convolutional Neural Networks (CNNs) has revolutionized the realm of image processing, leading to breakthroughs in numerous fields such as facial recognition, autonomous vehicles, and medical imaging. This can be attributed to their capacity for processing large-scale image data both efficiently and dependably. While CNN-based image processing techniques have played a significant role in feature extraction, information fusion, and the processing of static, dynamic, color, and grayscale images, it still holds immense potential for future advancements. Currently, more research is applying CNN-based image processing techniques to fields such as medical imaging, biometric identification, entertainment media, and public safety, presenting a variety of more refined and novel visual capabilities to individuals while simultaneously ensuring greater convenience.

Nevertheless, key challenges arise when CNNs are applied in image processing. These include the difficulty in handling complex and large-scale data, as well as the model's sensitivity to geometric transformations such as image deformation and rotation, which can lead to unstable prediction outcomes. In addition, the black-box nature of CNNs obscures the decision-making process, making it difficult to understand and interpret. Moreover, CNNs require a vast amount of annotated data for training, which can be challenging to obtain in certain fields like medical image processing, thereby limiting their application in these areas. Finally, just as federated learning has enhanced data security in computing networks, similar concerns and solutions are applicable to image processing using CNNs.

This Special Issue aims to provide a platform for researchers to present innovative and effective image processing technologies based on CNNs. This includes addressing the following specific topics:

Advancements in CNN-based image processing techniques;
Integration of CNNs with other AI techniques for image processing;
CNN architecture optimization for image processing;
Mathematical models for CNN-based image processing;
Security and privacy in image processing;
Resource allocation optimization for CNNs in image processing tasks;
Modeling, analysis, and measurement of computational and requirements for CNN-based image processing;
Interpretable image processing with CNNs.

Prof. Dr. Shaozhang Niu
Dr. Jiwei Zhang
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. 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.

Keywords

artificial intelligence
convolutional neural networks
deep learning
image processing
machine learning
information security
privacy-preserving
architecture optimization
multimedia

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 policies can be found here.

Related Special Issue

Image Processing Based on Convolution Neural Network: 2nd Edition in Electronics (1 article)

Published Papers (13 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

14 pages, 2369 KiB

Open AccessArticle

Supervised Face Tampering Detection Based on Spatial Channel Attention Mechanism

by Xinyi Wang, Wanru Song, Chuanyan Hao, Sijiang Liu and Feng Liu

Electronics 2025, 14(3), 500; https://doi.org/10.3390/electronics14030500 - 26 Jan 2025

Viewed by 605

Abstract

Face images hold exceptional significance in contemporary society, serving as direct identifiers due to their rich personal attributes, enhancing daily life and work efficiency. However, advancements in deep learning and image processing have led to the proliferation of sophisticated face forgery software, rendering detection increasingly challenging. We propose a novel face tampering detection method utilizing a spatial attention-enhanced bidirectional convolutional neural network to address this. This approach synergizes the strengths of dense convolutional and depthwise separable networks for superior image feature extraction, thereby improving the accuracy of authentic and manipulated face detection. Furthermore, the network is trained to initially localize tampered regions within face images by integrating a spatial channel-based attention module as supervisory input. On three widely used public face forgery datasets, our method achieves an AUC of no less than 96.45%. The experimental results validate the effectiveness of our method in accurately detecting and initially localizing face tampering. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

16 pages, 8261 KiB

Open AccessArticle

Enhanced Deblurring for Smart Cabinets in Dynamic and Low-Light Scenarios

by Yali Sun, Siyang Hu, Kai Xie, Chang Wen, Wei Zhang and Jianbiao He

Electronics 2025, 14(3), 488; https://doi.org/10.3390/electronics14030488 - 25 Jan 2025

Viewed by 550

Abstract

In this paper, we propose a novel method to address dynamic blur and low-light issues in smart cabinets, which is named the MIMO-IMF (Multi-input Multi-output U-Net Integrated Motion Framework). This method combines a Frequency-Domain Adaptive Fusion Module (FDAFM), built on the blind deblurring framework MIMO-UNet (Multi-input Multi-output U-Net), to improve the capture of high-frequency information and enhance the accuracy of blur region recovery. Additionally, a low-light luminance information extraction module (IFEM) is designed to complement the multi-scale features of the FDAFM by extracting valuable luminance information, significantly improving the efficiency of merchandise deblurring under low-light conditions. To further optimize the deblurring effect, we introduce an enhanced residual block structure and a novel loss function. The refined multi-scale residual block, combined with the FDAFM, better restores image details by refining frequency bands across different scales. The New Loss Function improves the model’s performance in low-light and dynamic blur scenarios by effectively balancing luminance and structural information. Experiments on the GOPRO dataset and the self-developed MBSI dataset show that our method outperforms the original model, achieving a PSNR improvement of 0.21 dB on the public dataset and 0.23 dB on the MBSI dataset. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

24 pages, 7874 KiB

Open AccessArticle

Power Insulator Defect Detection Method Based on Enhanced YOLOV7 for Aerial Inspection

by Jun Hu, Wenwei Wan, Peng Qiao, Yongqi Zhou and Aiguo Ouyang

Electronics 2025, 14(3), 408; https://doi.org/10.3390/electronics14030408 - 21 Jan 2025

Cited by 1 | Viewed by 738

Abstract

As a principal insulating component in power transmission systems, the integrity of the insulator is of paramount importance for ensuring the safe and reliable operation of transmission lines. While the deployment of aerial photography technology has markedly enhanced the efficacy of power facility inspections, the intricate backgrounds, multifarious viewpoint alterations, and erratic lighting circumstances inherent in the captured images present novel challenges for the algorithmic detection of insulator defects. To address these issues, this study proposes an enhanced version of the YOLOV7 detection algorithm. The introduction of the contextual transformer network (CoTNet) structure and an EMA attention mechanism enhances the model’s capacity to perceive global contextual information in images and to model long-distance feature dependencies. Experiments based on a real aerial photography dataset demonstrate that the proposed algorithm outperforms the benchmark model in all key performance indicators, including accuracy, recall, and F1 score, which improved by 0.6%, 1.8%, and 0.8%, respectively. Additionally, the average precision (mAP@[0.5]) and mAP@[0.5:0.95] improved by 0.6% and 4.4%, respectively. The superiority of the algorithm in feature extraction and target localization is verified through Grad-CAM visual analysis, which provides a high-precision detection method for intelligent inspection of power transmission systems. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

18 pages, 4627 KiB

Open AccessArticle

DBD-Net: Dual-Branch Decoder Network with a Multiscale Cascaded Residual Module for Ship Segmentation

by Xiajun Ding, Xiaodan Jiang and Xiaoliang Jiang

Electronics 2025, 14(1), 209; https://doi.org/10.3390/electronics14010209 - 6 Jan 2025

Viewed by 739

Abstract

The segmentation of visible ship images is an important part of intelligent ship monitoring systems. However, this task is faced with many difficulties in practical applications, such as complex background environments, variations in illumination, and target scale changes. In view of these situations, we present a dual-branch decoder network with a multiscale cascaded residual module for ship segmentation. Specifically, in the encoding stage, we introduce a multiscale cascaded residual module as a replacement for traditional convolution layers. By leveraging its multiscale architecture, the module effectively captures both the global context and fine-grained details. In the decoding phase, our framework incorporates two parallel branches, both of which utilize the cascading residual module to enhance feature extraction and representation. Additionally, one of the branches is equipped with spatial attention and channel attention mechanisms. Finally, comprehensive tests were conducted on the publicly available ship datasets MariBoatsSubclass and SeaShipsSeg. Our proposed network achieved impressive results, with Dice, Recall, Mcc, and Jaccard scores of 0.9003, 0.9105, 0.8706, and 0.8197 on the MariBoatsSubclass dataset. Similarly, it demonstrated outstanding performance on the SeaShipsSeg dataset, attaining Dice, Recall, Mcc, and Jaccard scores of 0.9538, 0.9501, 0.9519, and 0.9129, respectively. These results highlight the superior accuracy and robustness of DBD-Net in segmenting and detecting ships across diverse scenarios and datasets. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

21 pages, 5375 KiB

Open AccessArticle

PII-GCNet: Lightweight Multi-Modal CNN Network for Efficient Crowd Counting and Localization in UAV RGB-T Images

by Zuodong Niu, Huilong Pi, Donglin Jing and Dazheng Liu

Electronics 2024, 13(21), 4298; https://doi.org/10.3390/electronics13214298 - 31 Oct 2024

Viewed by 970

Abstract

With the increasing need for real-time crowd evaluation in military surveillance, public safety, and event crowd management, crowd counting using unmanned aerial vehicle (UAV) captured images has emerged as an essential research topic. While conventional RGB-based methods have achieved significant success, their performance is severely hampered in low-light environments due to poor visibility. Integrating thermal infrared (TIR) images can address this issue, but existing RGB-T crowd counting networks, which employ multi-stream architectures, tend to introduce computational redundancy and excessive parameters, rendering them impractical for UAV applications constrained by limited onboard resources. To overcome these challenges, this research introduces an innovative, compact RGB-T framework designed to minimize redundant feature processing and improve multi-modal representation. The proposed approach introduces a Partial Information Interaction Convolution (PIIConv) module to selectively minimize redundant feature computations and a Global Collaborative Fusion (GCFusion) module to improve multi-modal feature representation through spatial attention mechanisms. Empirical findings indicate that the introduced network attains competitive results on the DroneRGBT dataset while significantly reducing floating-point operations (FLOPs) and improving inference speed across various computing platforms. This study’s significance is in providing a computationally efficient framework for RGB-T crowd counting that balances accuracy and resource efficiency, making it ideal for real-time UAV deployment. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

21 pages, 4783 KiB

Open AccessArticle

CECL-Net: Contrastive Learning and Edge-Reconstruction-Driven Complementary Learning Network for Image Forgery Localization

by Gaoyuan Dai, Kai Chen, Linjie Huang, Longru Chen, Dongping An, Zhe Wang and Kai Wang

Electronics 2024, 13(19), 3919; https://doi.org/10.3390/electronics13193919 - 3 Oct 2024

Cited by 1 | Viewed by 1262

Abstract

While most current image forgery localization (IFL) deep learning models focus primarily on the foreground of tampered images, they often neglect the essential complementary background semantic information. This oversight tends to create significant gaps in these models’ ability to thoroughly interpret and understand a tampered image, thereby limiting their effectiveness in extracting critical tampering traces. Given the above, this paper presents a novel contrastive learning and edge-reconstruction-driven complementary learning network (CECL-Net) for image forgery localization. CECL-Net enhances the understanding of tampered images by employing a complementary learning strategy that leverages foreground and background features, where a unique edge extractor (EE) generates precise edge artifacts, and edge-guided feature reconstruction (EGFR) utilizes the edge artifacts to reconstruct a fully complementary set of foreground and background features. To carry out the complementary learning process more efficiently, we also introduce a pixel-wise contrastive supervision (PCS) method that attracts consistent regions in features while repelling different regions. Moreover, we propose a dense fusion (DF) strategy that utilizes multi-scale and mutual attention mechanisms to extract more discriminative features and improve the representational power of CECL-Net. Experiments conducted on two benchmark datasets, one Artificial Intelligence (AI)-manipulated dataset and two real challenge datasets, indicate that our CECL-Net outperforms seven state-of-the-art models on three evaluation metrics. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

15 pages, 8441 KiB

Open AccessArticle

PGD-Trap: Proactive Deepfake Defense with Sticky Adversarial Signals and Iterative Latent Variable Refinement

by Zhong Zhuang, Yoichi Tomioka, Jungpil Shin and Yuichi Okuyama

Electronics 2024, 13(17), 3353; https://doi.org/10.3390/electronics13173353 - 23 Aug 2024

Viewed by 1821

Abstract

With the development of artificial intelligence (AI), deepfakes, in which the face of one person is changed to another expression of the same person or a different person, have advanced. There is a need for countermeasures against crimes that exploit deepfakes. Methods to interfere with deepfake generation by adding an invisible weak adversarial signal to an image have been proposed. However, there is a problem: the weak signal can be easily removed by processing the image. In this paper, we propose trap signals that appear in response to a process that weakens adversarial signals. We also propose a new type of adversarial signal injection that allow us to reconstruct and change the original image as far as people do not feel strange by Denoising Diffusion Probabilistic Model (DDPM)-based Iterative Latent Variable Refinement. In our experiments with Star Generative Adversarial Network (StarGAN) trained with the CelebFaces Attributes (CelebA) Dataset, we demonstrate that the proposed approach achieves more robust proactive deepfake defense. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

29 pages, 9748 KiB

Open AccessArticle

Hybrid Machine Learning for Automated Road Safety Inspection of Auckland Harbour Bridge

by Munish Rathee, Boris Bačić and Maryam Doborjeh

Electronics 2024, 13(15), 3030; https://doi.org/10.3390/electronics13153030 - 1 Aug 2024

Viewed by 2134

Abstract

The Auckland Harbour Bridge (AHB) utilises a movable concrete barrier (MCB) to regulate the uneven bidirectional flow of daily traffic. In addition to the risk of human error during regular visual inspections, staff members inspecting the MCB work in diverse weather and light conditions, exerting themselves in ergonomically unhealthy inspection postures with the added weight of protection gear to mitigate risks, e.g., flying debris. To augment visual inspections of an MCB using computer vision technology, this study introduces a hybrid deep learning solution that combines kernel manipulation with custom transfer learning strategies. The video data recordings were captured in diverse light and weather conditions (under the safety supervision of industry experts) involving a high-speed (120 fps) camera system attached to an MCB transfer vehicle. Before identifying a safety hazard, e.g., the unsafe position of a pin connecting two 750 kg concrete segments of the MCB, a multi-stage preprocessing of the spatiotemporal region of interest (ROI) involves a rolling window before identifying the video frames containing diagnostic information. This study utilises the ResNet-50 architecture, enhanced with 3D convolutions, within the STENet framework to capture and analyse spatiotemporal data, facilitating real-time surveillance of the Auckland Harbour Bridge (AHB). Considering the sparse nature of safety anomalies, the initial peer-reviewed binary classification results (82.6%) for safe and unsafe (intervention-required) scenarios were improved to 93.6% by incorporating synthetic data, expert feedback, and retraining the model. This adaptation allowed for the optimised detection of false positives and false negatives. In the future, we aim to extend anomaly detection methods to various infrastructure inspections, enhancing urban resilience, transport efficiency and safety. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

19 pages, 4256 KiB

Open AccessArticle

Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction

by Shudong Liu, Wenlong Zhong, Furong Guo, Jia Cong and Boyu Gu

Electronics 2024, 13(14), 2751; https://doi.org/10.3390/electronics13142751 - 13 Jul 2024

Viewed by 1663

Abstract

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mixed Residual Attention Block (MRA Block), combining channel attention and window-based self-attention to capture local details in images. Additionally, the Dual Reconstruction Feature Fusion (DRFF) module is designed to enhance the model’s adaptability to both inter-class and intra-class variations by integrating features of different scales across layers. Cosine similarity networks are employed for similarity measurement, enabling precise predictions. The experiments demonstrate that the proposed method achieves classification accuracies of 96.99%, 98.53%, and 89.78% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively, confirming the method’s efficacy in fine-grained classification tasks. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

13 pages, 7709 KiB

Open AccessArticle

An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection

by Changyu Cai, Jianglong Nie, Jie Tong, Zhao Chen, Xiangnan Xu and Zhouqiang He

Electronics 2024, 13(11), 2080; https://doi.org/10.3390/electronics13112080 - 27 May 2024

Cited by 1 | Viewed by 944

Abstract

To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing the parameters in the network. In order to make the network less sensitive to the targets with an unconventional pose, a module based on CSPNeXt and GSConv is designed and integrated with the ELAN module to extract deep features from the targets. Moreover, a WIoU (Wise Intersection over Union) loss function is utilized to enhance the ability of the YOLOv7-tiny model to detect objects with an unconventional pose and the interference of the background. Finally, the experimental results on human targets in transmission line inspection demonstrate that the proposed network improves detection confidence and reduces missed detection. Compared to the YOLOv7-tiny model, the proposed method promotes the performance of accuracy while reducing the amount of parameters. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

22 pages, 8881 KiB

Open AccessArticle

DCGAN-Based Image Data Augmentation in Rawhide Stick Products’ Defect Detection

by Shuhui Ding, Zhongyuan Guo, Xiaolong Chen, Xueyi Li and Fai Ma

Electronics 2024, 13(11), 2047; https://doi.org/10.3390/electronics13112047 - 24 May 2024

Viewed by 1354

Abstract

The online detection of surface defects in irregularly shaped products such as rawhide sticks, a kind of pet food, is still a challenge for the food industry. Developing deep learning-based detection algorithms requires a diverse defect database, which is crucial for artificial intelligence applications. Acquiring a sufficient amount of realistic defect data is challenging, especially during the beginning of product production, due to the occasional nature of defects and the associated costs. Herein, we present a novel image data augmentation method, which is used to generate a sufficient number of defect images. A Deep Convolution Generation Adversarial Network (DCGAN) model based on a Residual Block (ResB) and Hybrid Attention Mechanism (HAM) is proposed to generate massive defect images for the training of deep learning models. Based on a DCGAN, a ResB and a HAM are utilized as the generator and discriminator in a deep learning model. The Wasserstein distance with a gradient penalty is used to calculate the loss function so as to update the model training parameters and improve the quality of the generated image and the stability of the model by extracting deep image features and strengthening the important feature information. The approach is validated by generating enhanced defect image data and conducting a comparison with other methods, such as a DCGAN and WGAN-GP, on a rawhide stick experimental dataset. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

16 pages, 466 KiB

Open AccessArticle

ESFuzzer: An Efficient Way to Fuzz WebAssembly Interpreter

by Jideng Han, Zhaoxin Zhang, Yuejin Du, Wei Wang and Xiuyuan Chen

Electronics 2024, 13(8), 1498; https://doi.org/10.3390/electronics13081498 - 15 Apr 2024

Cited by 1 | Viewed by 1820

Abstract

WebAssembly code is designed to run in a sandboxed environment, such as a web browser, providing a high level of security and isolation from the underlying operating system and hardware. This enables the execution of untrusted code in a web browser without compromising the security and integrity of the user’s system. This paper discusses the challenges associated with using fuzzing tools to identify vulnerabilities or bugs in WebAssembly interpreters. Our approach, known as ESFuzzer, introduces an efficient method for fuzzing WebAssembly interpreters using an Equivalent-Statement concept and the Stack Repair Algorithm. The samples generated by our approach successfully passed code validation. In addition, we developed effective mutation strategies to enhance the efficacy of our approach. ESFuzzer has demonstrated its ability to generate code that achieves 100% WebAssembly validation testing and achieves code coverage that is more than twice that of libFuzzer. Furthermore, the 24-h experiment results show that ESFuzzer performs ten times more efficiently than libFuzzer. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Figure 1

14 pages, 4435 KiB

Open AccessArticle

Feature Reduction Networks: A Convolution Neural Network-Based Approach to Enhance Image Dehazing

by Haoyang Yu, Xiqin Yuan, Ruofei Jiang, Huamin Feng, Jiaxing Liu and Zhongyu Li

Electronics 2023, 12(24), 4984; https://doi.org/10.3390/electronics12244984 - 12 Dec 2023

Cited by 1 | Viewed by 1669

Abstract

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks. Full article

(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network)

► Show Figures

Journal Menu

Journal Browser

Image Processing Based on Convolution Neural Network

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (13 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI