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 >

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 >

Ghadah Naif Alwakid^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068666 - 10 November 2025

Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design.… More >

Binghong Zhang, Jialing Zhou, Xinye Zhou, Jia Zhao, Jinchun Zhu, Guangpeng Fan^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.068309 - 10 November 2025

Abstract Remote sensing image super-resolution technology is pivotal for enhancing image quality in critical applications including environmental monitoring, urban planning, and disaster assessment. However, traditional methods exhibit deficiencies in detail recovery and noise suppression, particularly when processing complex landscapes (e.g., forests, farmlands), leading to artifacts and spectral distortions that limit practical utility. To address this, we propose an enhanced Super-Resolution Generative Adversarial Network (SRGAN) framework featuring three key innovations: (1) Replacement of L1/L2 loss with a robust Charbonnier loss to suppress noise while preserving edge details via adaptive gradient balancing; (2) A multi-loss joint optimization strategy… More >

Yiqiao Gong^1,2, Chunlai Wu¹, Wenfeng Zheng^1,*, Siyu Lu³, Guangyu Xu⁴, Lijuan Zhang⁵, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2223-2252, 2025, DOI:10.32604/cmes.2025.072136 - 26 November 2025

Abstract Recent Super-Resolution (SR) algorithms often suffer from excessive model complexity, high computational costs, and limited flexibility across varying image scales. To address these challenges, we propose DDNet, a dynamic and lightweight SR framework designed for arbitrary scaling factors. DDNet integrates a residual learning structure with an Adaptively fusion Feature Block (AFB) and a scale-aware upsampling module, effectively reducing parameter overhead while preserving reconstruction quality. Additionally, we introduce DDNetGAN, an enhanced variant that leverages a relativistic Generative Adversarial Network (GAN) to further improve texture realism. To validate the proposed models, we conduct extensive training using the More >

Weiqiang Xin^1,2,3,#, Zheng Wang^4,#, Xi Chen^1,5, Yufeng Tang¹, Bing Li¹, Chunwei Tian^2,5,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2837-2849, 2025, DOI:10.32604/cmc.2025.069784 - 23 September 2025

Abstract Deep convolutional neural networks (CNNs) have demonstrated remarkable performance in video super-resolution (VSR). However, the ability of most existing methods to recover fine details in complex scenes is often hindered by the loss of shallow texture information during feature extraction. To address this limitation, we propose a 3D Convolutional Enhanced Residual Video Super-Resolution Network (3D-ERVSNet). This network employs a forward and backward bidirectional propagation module (FBBPM) that aligns features across frames using explicit optical flow through lightweight SPyNet. By incorporating an enhanced residual structure (ERS) with skip connections, shallow and deep features are effectively integrated,… More >

Parvathaneni Naga Srinivasu^1,2, G. JayaLakshmi³, Sujatha Canavoy Narahari⁴, Victor Hugo C. de Albuquerque², Muhammad Attique Khan⁵, Hee-Chan Cho⁶, Byoungchol Chang^7,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2117-2139, 2025, DOI:10.32604/cmc.2025.065232 - 29 August 2025

Abstract The generation of high-quality, realistic face generation has emerged as a key field of research in computer vision. This paper proposes a robust approach that combines a Super-Resolution Generative Adversarial Network (SRGAN) with a Pyramid Attention Module (PAM) to enhance the quality of deep face generation. The SRGAN framework is designed to improve the resolution of generated images, addressing common challenges such as blurriness and a lack of intricate details. The Pyramid Attention Module further complements the process by focusing on multi-scale feature extraction, enabling the network to capture finer details and complex facial features… More >

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

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2699-2716, 2025, DOI:10.32604/cmc.2025.064541 - 03 July 2025

Abstract Infrared imaging technology has been widely adopted in various fields, such as military reconnaissance, medical diagnosis, and security monitoring, due to its excellent ability to penetrate smoke and fog. However, the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents. In addition, deploying super-resolution models on resource-constrained devices faces significant challenges. To address these issues, this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism. The network’s dynamic weighting module automatically adjusts the weights of the attention and non-attention branch outputs based on the… More >

Adéla Hamplová^*, Tomáš Novák, Miroslav Žáček, Jiří Brožek

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3329-3349, 2025, DOI:10.32604/cmes.2025.066025 - 30 June 2025

Abstract This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of… More >

Huansha Wang^*, Ruiyang Huang^*, Qinrang Liu, Shaomei Li, Jianpeng Zhang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 761-783, 2025, DOI:10.32604/cmc.2025.060395 - 26 March 2025

Abstract Multi-modal knowledge graph completion (MMKGC) aims to complete missing entities or relations in multi-modal knowledge graphs, thereby discovering more previously unknown triples. Due to the continuous growth of data and knowledge and the limitations of data sources, the visual knowledge within the knowledge graphs is generally of low quality, and some entities suffer from the issue of missing visual modality. Nevertheless, previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing. In this case, mainstream MMKGC models only use… More >