By Taha KhanOct 31 2022Reviewed by Sophia CoveneyImportance of Imaging Techniques for Marine Monitoring
Maritime catastrophes and marine disasters often happen in coastal seas worldwide due to harsh weather conditions and monitoring challenges, seriously harming the growth of the marine sector.
Several elements, such as real-time functions, clarity, and low cost, should be considered to produce a cost-effective solution for intelligent ocean observation and detection in a complicated backdrop. Therefore, studying innovative imaging techniques to monitor marine targets in several dimensions and at high resolution under challenging sea conditions is vital.
Infrared Imaging Technology
With the quick advancement of science and technology, infrared imaging technology has drawn significant attention from all over the world, especially when it comes to the medium and long wavelength bands, which are used for a variety of real-world applications because of their incredibly powerful radiation ability.
The medium and long wave dual band infrared imaging technology may concurrently gather data on infrared radiation from two medium and long wave atmospheric windows, providing more precise target information and enhancing the photoelectric system's capacity to adapt to various challenging environmental circumstances.
It has many significant uses in the military, medical sector, coastal monitoring, and other disciplines.
Improving Quality and Resolution of Infrared Images
Researchers have never ceased looking for improved techniques to increase the quality and resolution of infrared pictures to satisfy the pressing demand from the industrial side. The high-resolution (HR) and low-resolution (LR) databases are first created as part of the machine learning-based super-resolution reconstruction approach to categorize photos.
Machine learning is then used to develop a mapping connection between the two images. Using a learned mapping connection between HR and LR, the HR pictures of each output target may be directly acquired for the LR images of each input target. Deep learning-based picture super-resolution restoration has emerged as a research hotspot as new techniques continue to develop.
Developing Infrared Imaging Optical System
In this study, researchers developed a medium and long-wavelength infrared imaging optical system capable of offering a straightforward, lightweight, yet powerful focusing ability using an outfitted large relative aperture by utilizing new infrared optical materials and single-point diamond turning technology. The device produced good picture outputs in the middle and long infrared bands.
In addition, the researchers put forward a super-resolution network model with a feature extraction layer, an information extraction block, and a reconstruction block that combines infrared and visible light pictures. The model combines the feature information extracted from infrared and visible light photos, using the visible light image’s crucial feature information on the edge and texture to make up for the infrared image’s lack of detail and recreate the high-definition infrared image.
How the Study was Conducted
Infrared Optical Imaging System
The researchers created a wide-band infrared optical imaging system with a large relative aperture. The optical system’s focal length to aperture ratio hit 0.9. The system had a focal length of 30 mm, a detector pixel size of 15 μm, and a resolution of 640x512.
Network Model
On a visible light picture (RGB) and matching infrared image, the suggested network model is configured as a two-line operation (IR). It comprises a reconstruction block, an information extraction block, and a feature extraction layer.
The feature extraction layer uses two 3x3 convolution processes, followed by activation functions, to extract the shallow features from the original low-resolution picture and the visible light image.
The feature mapping attention mechanism is introduced in the information extraction block in the RGB line to extract the key feature information from the visible light picture and transfer it across channels to fill in the gaps in the IR information.
In addition, the IR line introduces the dense connection module to extract image characteristics, which can extract IR texture, edge, and other information more thoroughly.
Finally, the global feature data is incorporated to complete the IR reconstruction after the retrieved RGB and IR feature data are fused in the reconstruction block.
Conclusion
To improve the clarity of infrared pictures, the researchers created and processed a medium and long-wave infrared imaging optical system. Single point diamond turning technique was utilized to create brand-new infrared optical materials.
The system can generate experimental data to help our future modeling design and give easy, portable, and powerful focusing ability to employ the big relative aperture supplied.
In addition, researchers proposed a super-resolution network algorithm that can perform infrared image reconstruction, data fusion, and feature extraction by incorporating texture, edge, and other visible light details.
This algorithm uses deep learning to train a network model and simultaneously inputs paired infrared and visible images. According to experimental results, this approach successfully addresses the issue of poor-resolution optical infrared imaging in the marine environment.
Reference
Ren, Z., Zhao, J., Wang, C., Ma, X., Lou Y., & Wang, P. (2022) Research on Key Technologies of Super-Resolution Reconstruction of Medium and Long Wave Maritime Infrared Image. Applied Sciences, 12(21), 10871. https://www.mdpi.com/2076-3417/12/21/10871
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