Yifan Zhang¹, Yong Gan^2,*, Mengke Tang¹, Xinxin Gan³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068880 - 09 December 2025

Abstract High-resolution remote sensing imagery is essential for critical applications such as precision agriculture, urban management planning, and military reconnaissance. Although significant progress has been made in single-image super-resolution (SISR) using generative adversarial networks (GANs), existing approaches still face challenges in recovering high-frequency details, effectively utilizing features, maintaining structural integrity, and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery. To address these limitations, this paper proposes the Improved Residual Module and Attention Mechanism Network (IRMANet), a novel architecture specifically designed for remote sensing image reconstruction. IRMANet builds upon the Super-Resolution… More >

Xinwei Zhu, Jianxun Zhang^*, Huan Zeng

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068578 - 09 December 2025

Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >

Illia Khurtin^*, Mukesh Prasad

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-15, 2026, DOI:10.32604/cmc.2025.067481 - 09 December 2025

Abstract The field of artificial intelligence has advanced significantly in recent years, but achieving a human-like or Artificial General Intelligence (AGI) remains a theoretical challenge. One hypothesis suggests that a key issue is the formalisation of extracting meaning from information. Meaning emerges through a three-stage interpretative process, where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context. However, this approach currently lacks practical grounding. In this research, we developed a model based on contexts, which applies interpretation principles to the visual information to address this gap. The field of computer… More >

Hongji Chen, Jianxun Zhang^*, Tianze Yu, Yingzhu Zeng, Huan Zeng

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.067041 - 09 December 2025

Abstract Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination, alleviating the adverse effects of illumination degradation on image quality. Traditional Retinex-based approaches, inspired by human visual perception of brightness and color, decompose an image into illumination and reflectance components to restore fine details. However, their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results, particularly under extreme low-light scenarios. Although deep learning methods built upon Retinex theory have recently advanced the field, most still suffer from insufficient interpretability… More >

Nur Syaiful Afrizal¹, Khairul Adib Yusof^1,2,*, Lokman Hakim Muhamad¹, Nurul Shazana Abdul Hamid^2,3, Mardina Abdullah^2,4, Mohd Amiruddin Abd Rahman¹, Syamsiah Mashohor⁵, Masashi Hayakawa^6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-16, 2026, DOI:10.32604/cmc.2025.066421 - 09 December 2025

Abstract Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years. This study introduces a novel reconstruction-based modeling approach enhanced by negative learning, employing a Bidirectional Long Short-Term Memory (BiLSTM) network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals. By penalizing the model for accurately reconstructing seismic anomalies, the negative learning approach effectively magnifies the differences between normal and anomalous data. This strategic differentiation enhances the sensitivity of the BiLSTM network, enabling improved detection of subtle geomagnetic More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Xuanhong Wang¹, Hongyu Guo¹, Jiazhen Li¹, Mingchen Wang¹, Xian Wang¹, Yijun Zhang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069482 - 10 November 2025

Abstract Over the past decade, large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation. However, these models require enormous datasets and parameters, and their multi-step generation processes are often inefficient and difficult to control. To address these challenges, we propose CAFE-GAN, a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination, which incorporates a pre-trained CLIP model along with several key architectural innovations. First, we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation. Second, we introduce a trainable linear projection layer after the CLIP text… More >

Kim Sao Nguyen, Ngoc Dung Bui^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069355 - 10 November 2025

Abstract Reversible data hiding (RDH) enables secret data embedding while preserving complete cover image recovery, making it crucial for applications requiring image integrity. The pixel value ordering (PVO) technique used in multi-stego images provides good image quality but often results in low embedding capability. To address these challenges, this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image. The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order. Four secret bits are embedded into each block’s maximum pixel value, while three additional More >

Zhongliang Wei^1,*, Jianlong An¹, Chang Su²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069335 - 10 November 2025

Abstract Images taken in dim environments frequently exhibit issues like insufficient brightness, noise, color shifts, and loss of detail. These problems pose significant challenges to dark image enhancement tasks. Current approaches, while effective in global illumination modeling, often struggle to simultaneously suppress noise and preserve structural details, especially under heterogeneous lighting. Furthermore, misalignment between luminance and color channels introduces additional challenges to accurate enhancement. In response to the aforementioned difficulties, we introduce a single-stage framework, M2ATNet, using the multi-scale multi-attention and Transformer architecture. First, to address the problems of texture blurring and residual noise, we design… More >