Jiajia Liu^1,*, Junyi Lin², Wenxiang Dong², Xuan Zhao², Jianhua Liu², Huiru Li³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071397 - 09 December 2025

Abstract Single Image Super-Resolution (SISR) seeks to reconstruct high-resolution (HR) images from low-resolution (LR) inputs, thereby enhancing visual fidelity and the perception of fine details. While Transformer-based models—such as SwinIR, Restormer, and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information, these methods often suffer from substantial computational and memory overhead, which limits their deployment on resource-constrained edge devices. To address these challenges, we propose a novel lightweight super-resolution network, termed Binary Attention-Guided Information Distillation (BAID), which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter… More >

Mo Hou^1,2,3,#,*, Bin Xu^4,#, Wen Shang^1,2,3

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071282 - 09 December 2025

Abstract Image captioning, a pivotal research area at the intersection of image understanding, artificial intelligence, and linguistics, aims to generate natural language descriptions for images. This paper proposes an efficient image captioning model named Mob-IMWTC, which integrates improved wavelet convolution (IMWTC) with an enhanced MobileNet V3 architecture. The enhanced MobileNet V3 integrates a transformer encoder as its encoding module and a transformer decoder as its decoding module. This innovative neural network significantly reduces the memory space required and model training time, while maintaining a high level of accuracy in generating image descriptions. IMWTC facilitates large receptive… More >

Xu Tao¹, Qiang Xiao², Zhaoqi Jin², Hao Li^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070790 - 09 December 2025

Abstract Image fusion technology aims to generate a more informative single image by integrating complementary information from multi-modal images. Despite the significant progress of deep learning-based fusion methods, existing algorithms are often limited to single or dual-dimensional feature interactions, thus struggling to fully exploit the profound complementarity between multi-modal images. To address this, this paper proposes a parallel multi-dimensional complementary fusion network, termed PMCFusion, for the task of infrared and visible image fusion. The core of this method is its unique parallel three-branch fusion module, PTFM, which pioneers the parallel synergistic perception and efficient integration of… More >

Jia Yan Lim^1,2, Siti Madiha Muhammad Amir³, Roslan Yahya³, Marta Peña Fernández², Tze Chuen Yap^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070707 - 09 December 2025

Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… More >

Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Kirubavathi Ganapathiyappan¹, Heba G. Mohamed², Abhishek Yadav¹, Guru Akshya Chinnaswamy¹, Ateeq Ur Rehman^3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069951 - 09 December 2025

Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… More >

Cyreneo Dofitas¹, Yong-Woon Kim², Yung-Cheol Byun^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069374 - 09 December 2025

Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >

Agit Amrullah, Ferda Ernawan^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069229 - 09 December 2025

Abstract This paper proposes a tamper detection technique for semi-fragile watermarking using Quantization-based Discrete Cosine Transform (DCT) for tamper localization. In this study, the proposed embedding strategy is investigated by experimental tests over the diagonal order of the DCT coefficients. The cover image is divided into non-overlapping blocks of size 8 × 8 pixels. The DCT is applied to each block, and the coefficients are arranged using a zig-zag pattern within the block. In this study, the low-frequency coefficients are selected to examine the impact of the imperceptibility score and tamper detection accuracy. High accuracy of… More >

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 >