Multi-view imagefusion can be used to extract features from redundant and complementary multisource images. And the technique of obtaining high quality fusionimages has become one of the research hotspots for image ...
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Multi-view imagefusion can be used to extract features from redundant and complementary multisource images. And the technique of obtaining high quality fusionimages has become one of the research hotspots for image processing. In order to realize defect detection and intelligent grinding smoothly, multi-view fusion technology was applied in the field of overexposure and underexposure industrial welds, achieving high quality image enhancement. When preparing the data set of multi-view images, a hybrid registration algorithm with high matching ability is proposed. The data set of overexposure and underexposure weld images was obtained successfully by using the registration algorithm. In order to improve the fusion ability of overexposure and underexposure industrial welds, we propose a novel multi-view imagefusionalgorithm based on deep learning. The multi-view fusionalgorithm uses an autoencoder network structure, and its innovation lies in a parallel branch network with lightweight structure and strong generalization ability. The experimental results demonstrate that compared with other classical multi-view algorithms, our proposed algorithm gets the best parameters on the industrial weld data set in peak signal to noise ratio (PSNR) and root mean square error (RMSE), reaching 59.12 and 0.084, respectively. And the ablation and performance comparison experiments verify that the proposed parallel branch network has better generalization ability and fusion accuracy than other classical multi branch networks.
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