Dim infrared image enhancement based on convolutional neural network
| Autor: | Linyuan He, Zunlin Fan, Ma Shiping, Wenshan Ding, Duyan Bi, Xiong Lei |
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| Rok vydání: | 2018 |
| Předmět: |
Infrared image
Infrared Computer science Cognitive Neuroscience media_common.quotation_subject 02 engineering and technology 01 natural sciences Convolutional neural network 010309 optics Artificial Intelligence 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Contrast (vision) Computer vision media_common Mathematics::Commutative Algebra Artificial neural network Spatial filter business.industry Computer Science Applications Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) 020201 artificial intelligence & image processing Artificial intelligence business MNIST database |
| Zdroj: | Neurocomputing. 272:396-404 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2017.07.017 |
| Popis: | Long-range infrared images are always suffering from dim targets and background clutters. To improve the contrast between target and background, we propose a novel infrared image enhancement approach by highlighting target and suppressing background clutters. Predicting the target and background plays a key role in improving the contrast of dim infrared images that targets are embedded by background clutters. Taking full advantage of machine learning on prediction, we design the convolutional neural network (CNN) architecture in our study. To overcome the lack of large training data, the handwritten images in MNIST dataset are employed to simulate the properties of long-rang infrared images including dim targets, background clutters and low contrast. The target and background sub-images are predicted from the original dim infrared image based on the filters in the first layer of the trained CNN. Finally, the dim infrared image is enhanced by amplifying the targets and subtracting background clutters. The results of subjective and quantitative tests prove the performance of the proposed algorithm in contrast improvement. |
| Databáze: | OpenAIRE |
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