- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Single infrared image enhancement using a deep convolutional neural network
摘要： In this paper, we propose a deep learning method for single infrared image enhancement. A fully convolutional neural network (CNN) is used to produce images with enhanced contrast and details. The conditional generative adversarial networks are incorporated into the optimization framework to avoid the background noise being amplified and further enhance the contrast and details. The existing convolutional neural network architectures, such as residual architectures and encoder–decoder architectures, fail to achieve the best results both in terms of network performance and application scope for infrared image enhancement task. To address this problem, we specifically design a new refined convolutional neural architecture that produces visually very appealing results with higher contrast and sharper details compared to other network architectures. Visible images are used for training since there are fewer infrared images. Proper training samples are generated to ensure that the network trained on visible images can be well applied to infrared images. Experiments demonstrate that our approach outperforms existing image enhancement algorithms in terms of contrast and detail enhancement. Code is available at https://github.com/Kuangxd/IE-CGAN.
关键词： Residual network,Enhancement,Infrared images,Deep learning,Encoder–decoder network,Generative adversarial network
更新于2025-09-23 15:23:52
-
[IEEE 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) - Aqaba, Jordan (2018.10.28-2018.11.1)] 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) - Number of Texture Unit as Feature to Breast's Disease Classification from Thermal Images
摘要： This paper presents the use of the Number of Texture Unit as a feature extractor for classification of breast images. The Number of Texture Unit served as the basis for the idealization of the Local Binary Pattern a technique that is widely used in facial recognition. We compared the proposed strategy with the Gray Level Co-occurrence Matrix which is the most used texture analysis technique in the literature. With this work we have been able to show that the combination of the two techniques of feature extraction improves the final result of classification. To perform the tests we used the Support Vectors Machine classifier and obtained a result of 96.15% Area Under the Curve (Receiver Operating Characteristic Curve).
关键词： computer aided diagnosis,machine learning,support vector machine,feature extraction,infrared images,Local Binary Pattern
更新于2025-09-19 17:15:36
-
Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning
摘要： With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.
关键词： Isolated deep learning,Develop-model transfer deep learning,Automatic defect detection,Thermography,Infrared images,Photovoltaic (PV) modules
更新于2025-09-19 17:13:59
-
[IEEE 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) - Cluj-Napoca (2018.9.6-2018.9.8)] 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP) - A Deep Learning Approach For Pedestrian Segmentation In Infrared Images
摘要： Semantic segmentation in the context of traffic scenes has been vastly explored using different architectures for deep convolutional networks and color images. In the case of infrared images there is place for improvement and scientific contributions mainly due to the lack of data sets that contain baseline segmentations in the infrared domain. This paper proposes a method for real time infrared pedestrian segmentation using ERFNet. Within the context of the proposed method we study the effect of different basic image enhancement techniques on the performance of the segmentation. We enhance an existing dataset of infrared images with ground truth segmentations for pedestrians. Our experiments show that the proposed method is accurate and appropriate for real time applications.
关键词： pedestrian segmentation,ERFNet,infrared images,deep learning,image enhancement
更新于2025-09-04 15:30:14