Abstract
Hot-spot effect is well-known in the practical application of solar cells. The faults of hot spots seriously affect the performance and service life of solar cells. In order to highlight the infrared and visible complementary information of hot spots and discover more details, this paper presents a novel method, which fuses an infrared image saliency map based on random rectangular regions of interest with visible image by NonSubsampled Contourlet Transform (NSCT). Firstly, the infrared hot spots image was detected by saliency map which is randomly sampled. Secondly, the infrared and visible image of the hot spots are transformed into high-frequency and low-frequency sub-bands by NSCT. Finally, absolute value maximization and saliency map guidance are used to fuse low-frequency and high-frequency sub-bands respectively. The experimental results demonstrate that, compared with a set of related fusion methods, the presented method can better highlight the hot spots and other abundant information in visibility. It achieves accurate defects locating ability and has better performance on subjective and objective quality evaluation.
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