In the conventional infrared-polarization fusion method based on HSV color space, the problem of low color contrast and information loss or artifacts always exist which would impact the observation and processing of the fusion results. The cause of low color contrast in conventional method is analyzed in this paper, and an improved method is proposed. We find that some improvements could be done based on Channel S and Cannel V separately which could enhance the details in the regions of interest and maintain the precision of image as well. For Channel S, saliency extraction and morphological filtering are implemented to obtain the salient features of intensity image and degree of polarization image, while the redundant background information could be suppressed. The detail enhancement in Channel S could be realized through the fusion of common and unique feature of the salient images extracted above. For Channel V, the common and unique feature fusion between the salient degree of polarization image and intensity image could enhance the details in Channel V. Experimental results have proved that the method we illustrate could obtain better color contrast and less artifacts compared to the conventional fusion methods.
Underwater optical imaging has important application value, but it is also challenging. In traditional underwater imaging, the problems of uneven illumination, blurred texture details and low contrast often exist, in this paper we propose an underwater active polarization imaging algorithm based on low-rank sparse decomposition aiming to solve the problems above. According to the principle of underwater polarization imaging, the algorithm first performs target information enhancement on the acquired polarization images. Then combining with the low-rank characteristics of backscatter images in the scattered light field, the background information and target information could be separated from the captured images by the low-rank sparse decomposition principle, the high-quality image could be recovered from turbid water as a result. The results of experimental treatments with different turbidity levels demonstrate that the underwater polarization imaging algorithm based on low-rank sparse decomposition can improve the contrast of images, maintain the details of images and remove the background scattering at the same time. Moreover, the proposed method can effectively recover multiple targets and significantly improve the imaging quality which provides a new idea for underwater polarization clear imaging detection.
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