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Image processing techniques for viewpoint correction and resolution enhancement in light field 3D displays 光场三维显示器视点校正和分辨率增强的图像处理技术

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-22 DOI: 10.1016/j.displa.2025.103295

Wonseok Son, Youngrok Kim, Sung-Wook Min

引用次数: 0

Exp-FFCNN: Explainable feature fusion convolutional neural network for lung cancer classification Exp-FFCNN：用于肺癌分类的可解释特征融合卷积神经网络

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-10 DOI: 10.1016/j.displa.2025.103318

Muhammad Sufyan , Jun Qian , Jianqiang Li , Abdul Qadir Khan , Azhar Imran

{"title":"Exp-FFCNN: Explainable feature fusion convolutional neural network for lung cancer classification","authors":"Muhammad Sufyan , Jun Qian , Jianqiang Li , Abdul Qadir Khan , Azhar Imran","doi":"10.1016/j.displa.2025.103318","DOIUrl":"10.1016/j.displa.2025.103318","url":null,"abstract":"<div><div>Precise detection of lung cancer type is crucial for effective treatment, but blurred edges and textures of lung nodules can lead to misclassification, resulting in inappropriate treatment strategies. To address this challenge, we propose an explainable feature fusion convolutional neural network (Exp-FFCNN). The Exp-FFCNN model incorporates convolutional blocks with atrous spatial pyramid pooling (ASPP), squeeze-and-excitation ConvNeXt blocks (SECBs), and a feature fusion head block (FFHB). The initial convolutional blocks, augmented by ASPP, enable precise extraction of multi-dimensional local and global features of lung nodules. The SECBs are designed to capture domain-specific information by extracting deep and detailed texture features using an attention mechanism that highlights blurred textures and shape features. A pre-trained VGG16 feature extractor is utilized for diverse edge-related feature maps then both feature’s maps are fused channel-wise and spatial-wise in FFHB. To improve input image quality, several preprocessing techniques are applied, and to mitigate class imbalance, the borderline synthetic minority oversampling technique (BORDERLINE SMOTE) is employed. The chest CT-scan images dataset is used for training, while the generalizability of the model is validated on IQ-OTH/NCCD dataset. Through comprehensive evaluation against state-of-the-art models, our framework demonstrates exceptional accuracy, achieving 99.60% on the chest CT-scan images dataset and 98% on the IQ-OTH/NCCD dataset. Furthermore, to enhance feature interpretability for radiologists, Grad-CAM and LIME are utilized. This explainability provides insight into the decision-making process, improving the transparency and interpretability of the model, thereby fostering greater confidence in its application for real-world lung cancer diagnoses.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103318"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on CT image deblurring method based on focal spot intensity distribution 基于焦斑强度分布的CT图像去模糊方法研究

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-21 DOI: 10.1016/j.displa.2025.103291

Fengxiao Li , Guowei Zhong , Haijun Yu , Rifeng Zhou

{"title":"Research on CT image deblurring method based on focal spot intensity distribution","authors":"Fengxiao Li , Guowei Zhong , Haijun Yu , Rifeng Zhou","doi":"10.1016/j.displa.2025.103291","DOIUrl":"10.1016/j.displa.2025.103291","url":null,"abstract":"<div><div>The finite focal spot of the X-ray source is a fundamental physical bottleneck limiting the spatial resolution of Computed Tomography (CT), as its penumbra blurring severely degrades image detail discernibility. To overcome this limitation, this paper proposes a physics-informed deblurring method. Firstly, to circumvent the challenge of acquiring ideal reference images in real scenarios, we developed a high-fidelity physical forward model to generate a high-quality paired dataset with the help of precise measurement of the focal spot’s 2D intensity distribution by the circular hole edge response backprojection method. Secondly, to learn the inverse mapping from blurred projections to ideal projections, we designed an Enhanced Phase U-Net (EPU-Net) deep learning network, which contains an innovative Eulerian Phase Unit (EPU) module. This module transforms feature maps into the Fourier domain, leveraging the high-fidelity structural information carried by the phase spectrum. Through a phase-attention-driven mechanism, it guides and rectifies the amplitude spectrum information corrupted during blurring. This mechanism enables the network to accurately restore the high-frequency components crucial to image details. Both simulated and physical experiments illustrate that EPU-Net outperforms state-of-the-art algorithms such as RCAN and CMU-Net in terms of Peak Signal-to-Noise Ratio and Feature Similarity. More importantly, in visual quality assessments, EPU-Net successfully restored fine structures indistinguishable by other methods, demonstrating exceptional deblurring performance and robust generalization capability. This study presents a novel approach combining physics-model-driven data generation and deep network-based inverse solution learning to enhance image quality in high-resolution CT systems.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103291"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision language model based panel digit recognition for medical screen data acquisition 基于视觉语言模型的面板数字识别医学屏幕数据采集

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-11 DOI: 10.1016/j.displa.2025.103282

Yizhi Zou , Shuangjie Yuan , Haoyu Liu , Xu Cheng , Tao Zhu , Lu Yang

{"title":"Vision language model based panel digit recognition for medical screen data acquisition","authors":"Yizhi Zou , Shuangjie Yuan , Haoyu Liu , Xu Cheng , Tao Zhu , Lu Yang","doi":"10.1016/j.displa.2025.103282","DOIUrl":"10.1016/j.displa.2025.103282","url":null,"abstract":"<div><div>In the cardiac operating room, physicians interpret patients’ vital signs from medical equipment to make critical decisions, such as administering blood transfusions. However, the absence of automated data acquisition in these devices significantly complicates the documentation of surgical information. Existing text recognition methods are often limited to single applications and lack broad generalization capabilities, with inconsistent detection and recognition times. We present a novel medical device screen recognition framework based on pretrained Vision Language Models (VLMs). The structure based on the vision language model significantly enhances the flexibility of the application scenario, and the multi-round dialogue is more humanized, allowing for a better understanding of the surgeon’s needs. Considering the existence of unclear image information acquired by a head-mounted camera, for the acquisition of screen data, we propose Medical Screen Data Acquisition-VLM (MSDA-VLM) with a pre-filtering module to detect image blur. This module detects heavy ghost images via Local Binary Pattern (LBP) based texture block matching and assesses image sharpness through the variance of deep feature maps. Trained by thousands of screen images on the pretrained VLM, we achieve a 17.07% improvement in precision and a 17.05% improvement in recall. Furthermore, the experiment results demonstrate notable enhancements in medical screen data recognition.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103282"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A user experience evaluation framework for collaborative robot based on DEMATEL-ISM method 基于DEMATEL-ISM方法的协作机器人用户体验评价框架

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-26 DOI: 10.1016/j.displa.2025.103293

Guannan Li , Jingchen Cong , Binbin Lian , Tao Sun

{"title":"A user experience evaluation framework for collaborative robot based on DEMATEL-ISM method","authors":"Guannan Li , Jingchen Cong , Binbin Lian , Tao Sun","doi":"10.1016/j.displa.2025.103293","DOIUrl":"10.1016/j.displa.2025.103293","url":null,"abstract":"<div><div>Collaborative robots designed to perform intricate tasks through human-robot collaboration have been globally adopted in manufacturing systems. While prior studies emphasize factors affecting user experience during collaboration, existing evaluation frameworks lack systematic approaches to quantify user experience and clarify the hierarchical relationships among these factors. This study addresses this limitation by developing a novel evaluation framework that integrates the Decision-Making Trial and Evaluation Laboratory method and the Interpretative Structural Modeling method. The framework identifies critical human-robot collaboration factors, establishes their causal relationships, and introduces a computational model to quantify user experience scores. A case study involving a parallel collaborative robot for aircraft cabin kitchen assembly demonstrated the framework’s practical utility in guiding user experience optimization. This advancement equips manufacturers with a structured tool to enhance the user experience of collaborative robot by harmonizing technical efficiency with human-centered usability requirements.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103293"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient and accurate 3D foot measurement method from smartphone image sequences 高效、准确的智能手机图像序列三维足部测量方法

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-07 DOI: 10.1016/j.displa.2025.103316

Xiaojie Hou, Renyan Hong, Tianyu Wang, Yuyou Zhang, Junfeng Li

{"title":"Efficient and accurate 3D foot measurement method from smartphone image sequences","authors":"Xiaojie Hou, Renyan Hong, Tianyu Wang, Yuyou Zhang, Junfeng Li","doi":"10.1016/j.displa.2025.103316","DOIUrl":"10.1016/j.displa.2025.103316","url":null,"abstract":"<div><div>Foot health is closely related to the comfort of shoes. Currently, shoe size settings are primarily based on the sales volume of shoe types, designed in segments to fit the foot shapes of the majority. When shopping online, consumers choose shoe types and sizes based on their foot length and personal experience, a single-dimensional matching method that often leads to inappropriate shoe selection and subsequently causes foot health issues. 3D reconstruction technology can accurately measure foot parameters, enabling consumers to make personalized shoe choices based on multi-dimensional foot measurements, effectively reducing health risks caused by unsuitable shoe types. Hence, this study proposes an efficient and robust 3D foot reconstruction technology based on smartphone image sequences. Initially, by extracting scale-invariant feature transform (SIFT) points and using an improved structure from motion (SfM) algorithm, this study generated a sparse point cloud of the foot. Subsequently, a multi-view stereo (MVS) algorithm was utilized to integrate depth and normal vector information for densifying the foot point cloud. Finally, a simple and efficient automated method for measuring foot parameters was designed, which measured the generated foot point cloud, achieving the measurement of key foot parameters including foot length, width, ball girth, and heel width with an error of less than 2 mm. This method makes foot measurement convenient and accurate, thereby supporting personalized shoe selection and recommendation.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103316"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Industrial Park Anomaly Detection: A virtual-real dataset and an attention-enhanced YOLO model via knowledge distillation 工业园区异常检测：基于知识蒸馏的虚实数据集和注意力增强YOLO模型

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-29 DOI: 10.1016/j.displa.2025.103301

Qifan Zhu , Li Yang , Li Zhang , Jian Wu , Feng Shao , Hongjie Shen

{"title":"Industrial Park Anomaly Detection: A virtual-real dataset and an attention-enhanced YOLO model via knowledge distillation","authors":"Qifan Zhu , Li Yang , Li Zhang , Jian Wu , Feng Shao , Hongjie Shen","doi":"10.1016/j.displa.2025.103301","DOIUrl":"10.1016/j.displa.2025.103301","url":null,"abstract":"<div><div>Anomaly detection in industrial parks has long been challenged by the scarcity of training samples and limited feature extraction capabilities. To overcome the limitation of scarce real-world incident records, we construct a Virtual Industrial Park Anomaly Detection Dataset (VIPAD-3K), a comprehensive anomaly detection dataset that includes diverse accident types, camera viewpoints, and environmental conditions. Furthermore, we propose YOLO-based Industrial Park Anomaly Detection (YOLO-IPAD), a detection algorithm based on knowledge distillation framework integrating real-world and virtual data sources. The teacher network utilizes a large-scale real-world dataset to guide the student network in learning more effective feature representations, thereby improving its ability to detect smoke, worker falls, and safety helmet usage. Based on YOLOv8, we further enhance the model by incorporating Hierarchical Dilated Attention Mechanism and Cross-scale Feature Fusion modules, which adaptively emphasize spatially salient regions and channel-wise feature discrimination, thereby improving key feature extraction. The evaluation outcomes indicate that YOLO-IPAD attains an 89.2% accuracy rate in identifying anomalies within industrial park scenarios. This performance surpasses multiple cutting-edge techniques, highlighting the reliability and real-world applicability of the proposed model.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103301"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Texture generation and adaptive fusion networks for image inpainting 纹理生成和自适应融合网络用于图像绘制

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-17 DOI: 10.1016/j.displa.2025.103287

Wuzhen Shi, Wu Yang, Yang Wen

{"title":"Texture generation and adaptive fusion networks for image inpainting","authors":"Wuzhen Shi, Wu Yang, Yang Wen","doi":"10.1016/j.displa.2025.103287","DOIUrl":"10.1016/j.displa.2025.103287","url":null,"abstract":"<div><div>Image inpainting aims to reconstruct missing regions in images with visually realistic and semantically consistent content. Existing deep learning-based methods often rely on structural priors to guide the inpainting process, but these priors provide limited information for texture recovery, leading to blurred or inconsistent details. To address this issue, we propose a Texture Generation and Adaptive Fusion Network (TGAFNet) that explicitly models texture priors to enhance high-frequency texture generation and adaptive fusion. TGAFNet consists of two branches: a main branch for coarse image generation and refinement, and a texture branch for explicit texture synthesis. The texture branch exploits both contextual cues and multi-level features from the main branch to generate sharp texture maps under the guidance of adversarial training with SN-PatchGAN. Furthermore, a Texture Patch Adaptive Fusion (TPAF) module is introduced to perform patch-to-patch matching and adaptive fusion, effectively handling cross-domain misalignment between the generated texture and coarse images. Extensive experiments on multiple benchmark datasets demonstrate that TGAFNet achieves state-of-the-art performance, generating visually realistic and fine-textured results. The findings highlight the effectiveness of explicit texture priors and adaptive fusion mechanisms for high-fidelity image inpainting, offering a promising direction for future image restoration research.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103287"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Teacher–student adversarial YOLO for domain adaptive detection in traffic scenes under adverse weather 基于师生对抗YOLO的恶劣天气交通场景域自适应检测

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-11-17 DOI: 10.1016/j.displa.2025.103289

Xuejuan Han , Zhong Qu , Shufang Xia

{"title":"Teacher–student adversarial YOLO for domain adaptive detection in traffic scenes under adverse weather","authors":"Xuejuan Han , Zhong Qu , Shufang Xia","doi":"10.1016/j.displa.2025.103289","DOIUrl":"10.1016/j.displa.2025.103289","url":null,"abstract":"<div><div>The problem of difficult traffic object localization under adverse weather has not been solved due to the labor-intensive and time-consuming process of collecting and labeling large-scale data. Domain adaptive object detection (DAOD) can achieve cross-domain detection without labeling, however, most of the existing DAOD methods are based on two-stage Faster R-CNN, which needs to be improved in both accuracy and speed. We propose a DAOD method TSA-YOLO, which takes full advantage of adversarial learning and pseudo-labeling to achieve high-performance cross-domain detection for fog, rain, and low-light scenes. For the input images, we generate auxiliary domain images by CycleGAN, also design a strong and weak enhancement method to reduce the bias of the teacher and student models. Additionally, in the student self-learning module, we propose a pixel-level domain discriminator to better extract domain-invariant features, effectively narrowing the feature distribution gap between the source and target domains. In the teacher–student mutual learning module, we incorporate the mean teacher (MT) model, iteratively update parameters to generate high-quality pseudo-labels. In addition, we evaluate our method on the public datasets Foggy Cityscapes, Rain Cityscapes, and BDD100k_Dark. The results show that TSA-YOLO significantly improves detection performance. Specifically, compared with the baseline, TSA-YOLO achieves up to a 15.0% increase in <em>[email protected]</em> on Foggy Cityscapes and up to an 18.5% increase on Rain Cityscapes, while converging in only 50 epochs and without reducing the model’s inference speed.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103289"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145580486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation on the bottom gate influence of four-terminal low temperature Poly-silicon thin film transistor 四端低温多晶硅薄膜晶体管底栅影响的研究

IF 3.4 2区工程技术

Displays Pub Date : 2026-04-01 Epub Date: 2025-12-15 DOI: 10.1016/j.displa.2025.103326

Yana Gao , Cong Peng , Kang Yang , Xifeng Li , Jianhua Zhang

{"title":"Investigation on the bottom gate influence of four-terminal low temperature Poly-silicon thin film transistor","authors":"Yana Gao , Cong Peng , Kang Yang , Xifeng Li , Jianhua Zhang","doi":"10.1016/j.displa.2025.103326","DOIUrl":"10.1016/j.displa.2025.103326","url":null,"abstract":"<div><div>In this letter, we fabricated four-terminal low-temperature polysilicon thin-film transistors with separated bottom shielding metal electrodes and top gate electrodes, and systematically investigated the effects of bottom gate bias (<em>V<sub>BGS</sub></em>) and gate insulator (GI) thickness on device characteristics. The results show that there is a linear relationship between the bottom gate voltage and the TFT threshold voltage (<em>V<sub>TH</sub></em>). For devices with a GI thickness of 140 nm, the slope of the <em>V<sub>TH</sub></em>-<em>V<sub>BGS</sub></em> curve is approximately −0.197. Reliability results indicate that a positive <em>V<sub>BGS</sub></em> shift (from −10 V to 10 V) reduces the temperature-induced <em>V<sub>TH</sub></em> shift by about 30.9 % and improves the negative bias temperature stress (NBTS) stability by approximately 17 %. These confirm that positive <em>V<sub>BGS</sub></em> enhances the temperature sensitivity and NBTS stability of TFTs by reducing trap states. On the other hand, reducing the GI thickness can enhance the top gate control capability, including suppressing the VTH shift caused by <em>V<sub>BGS</sub></em> voltage variations. Meanwhile, when the GI thickness is reduced to 120 nm, the difference in <em>V<sub>TH</sub></em> under NBTS between the two <em>V<sub>BGS</sub></em> conditions (−10 V and 10 V) narrows.</div></div>","PeriodicalId":50570,"journal":{"name":"Displays","volume":"92 ","pages":"Article 103326"},"PeriodicalIF":3.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0