Journal of Imaging

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Open AccessArticle

A Fast Nonlinear Sparse Model for Blind Image Deblurring

by Zirui Zhang, Zheng Guo, Zhenhua Xu, Huasong Chen, Chunyong Wang, Yang Song, Jiancheng Lai, Yunjing Ji and Zhenhua Li

J. Imaging 2025, 11(10), 327; https://doi.org/10.3390/jimaging11100327 (registering DOI) - 23 Sep 2025

Abstract

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on

L_{2}

L_{1}

, and

L_{p}

regularizations have been widely adopted. Based on this foundation [...] Read more.

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on

L_{2}

L_{1}

, and

L_{p}

regularizations have been widely adopted. Based on this foundation and combining successful experiences of previous work, this paper introduces

L_{N}

regularization, a novel nonlinear sparse regularization combining the

L_{p}

and

L_{\infty}

norms via nonlinear coupling. Statistical probability analysis demonstrates that

L_{N}

regularization achieves stronger sparsity than traditional regularizations like

L_{2}

L_{1}

, and

L_{p}

regularizations. Furthermore, building upon the

L_{N}

regularization, we propose a novel nonlinear sparse model for blind image deblurring. To optimize the proposed

L_{N}

regularization, we introduce an Adaptive Generalized Soft-Thresholding (AGST) algorithm and further develop an efficient optimization strategy by integrating AGST with the Half-Quadratic Splitting (HQS) strategy. Extensive experiments conducted on synthetic datasets and real-world images demonstrate that the proposed nonlinear sparse model achieves superior deblurring performance while maintaining completive computational efficiency. Full article

(This article belongs to the Topic Image Processing, Signal Processing and Their Applications)

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3784 KB

Open AccessReview

A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches

by Thandiwe Nyawose, Rito Clifford Maswanganyi and Philani Khumalo

J. Imaging 2025, 11(10), 326; https://doi.org/10.3390/jimaging11100326 - 23 Sep 2025

Abstract

The early and accurate detection of plant diseases is essential for ensuring food security, enhancing crop yields, and facilitating precision agriculture. Manual methods are labour-intensive and prone to error, especially under varying environmental conditions. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has advanced automated disease identification through image classification. However, challenges persist, including limited generalisability, small and imbalanced datasets, and poor real-world performance. Unlike previous reviews, this paper critically evaluates model performance in both lab and real-time field conditions, emphasising robustness, generalisation, and suitability for edge deployment. It introduces recent architectures such as GreenViT, hybrid ViT–CNN models, and YOLO-based single- and two-stage detectors, comparing their accuracy, inference speed, and hardware efficiency. The review discusses multimodal and self-supervised learning techniques to enhance detection in complex environments, highlighting key limitations, including reliance on handcrafted features, overfitting, and sensitivity to environmental noise. Strengths and weaknesses of models across diverse datasets are analysed with a focus on real-time agricultural applicability. The paper concludes by identifying research gaps and outlining future directions, including the development of lightweight architectures, integration with Deep Convolutional Generative Adversarial Networks (DCGANs), and improved dataset diversity for real-world deployment in precision agriculture. Full article

(This article belongs to the Section Image and Video Processing)

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19 pages, 6027 KB

Open AccessArticle

An Improved HRNetV2-Based Semantic Segmentation Algorithm for Pipe Corrosion Detection in Smart City Drainage Networks

by Liang Gao, Xinxin Huang, Wanling Si, Feng Yang, Xu Qiao, Yaru Zhu, Tingyang Fu and Jianshe Zhao

J. Imaging 2025, 11(10), 325; https://doi.org/10.3390/jimaging11100325 - 23 Sep 2025

Abstract

Urban drainage pipelines are essential components of smart city infrastructure, supporting the safe and sustainable operation of underground systems. However, internal corrosion in pipelines poses significant risks to structural stability and public safety. In this study, we propose an enhanced semantic segmentation framework based on High-Resolution Network Version 2 (HRNetV2) to accurately identify corroded regions in Traditional closed-circuit television (CCTV) images. The proposed method integrates a Convolutional Block Attention Module (CBAM) to strengthen the feature representation of corrosion patterns and introduces a Lightweight Pyramid Pooling Module (LitePPM) to improve multi-scale context modeling. By preserving high-resolution details through HRNetV2’s parallel architecture, the model achieves precise and robust segmentation performance. Experiments on a real-world corrosion dataset show that our approach attains a mean Intersection over Union (

m I o U

) of 95.92

\pm

0.03%,

R e c a l l

of 97.01

\pm

0.02%, and an overall

A c c u r a c y

of 98.54%. These results demonstrate the method’s effectiveness in supporting intelligent infrastructure inspection and provide technical insights for advancing automated maintenance systems in smart cities. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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J. Imaging, Volume 11, Issue 10 (October 2025) – 3 articles

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