Multispectral image processing algorithms for enhanced vision systems in the Arctic
Abstract The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images being subject to contrast enhancement parameters are estimated. Based on...
| Published in: | IOP Conference Series: Earth and Environmental Science |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
IOP Publishing
2019
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1088/1755-1315/302/1/012063 https://iopscience.iop.org/article/10.1088/1755-1315/302/1/012063/pdf https://iopscience.iop.org/article/10.1088/1755-1315/302/1/012063 |
| Summary: | Abstract The issues of enhanced vision multispectral systems application for robotic complexes control in the Arctic are considered. The existing contrast enhancement methods are observed. Probability characteristics of images being subject to contrast enhancement parameters are estimated. Based on these characteristics, the authors concluded that image areas requiring the greatest contrast enhancement are the areas with low saturation and magnitude gradients, at certain brightness values. Image quality improvement method is proposed. It performs processing only in the areas where it is necessary to enhance the contrast, practically without affecting the most homogeneous or structured image parts. The processed image saturation remains due to the processing of both luminance channel and saturation channel. The algorithm proposed also provides contrast enhancement of shaded image areas. The calculated values of various objective image quality indices indicate that the contrast enhancement algorithm proposed provides better results than known approaches. In addition, different spectral range image fusion algorithm ensuring visibility in the presence of interfering factors is proposed. It differs from known methods by adaptive weight adjustment in different areas of image. The example confirming the effectiveness of the fusion method proposed is shown. For its comparison with known methods, the values of fusion objective quality indices are calculated. The fusion algorithm proposed is shown to surpass known methods by various quality assessments. The conclusion about the expediency of using the algorithms developed in technical vision systems of robotic complexes in the Arctic is made. |
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