24 October 2017 Multi-exposure high dynamic range image synthesis with camera shake correction
Author Affiliations +
Proceedings Volume 10458, AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing; 104580I (2017) https://doi.org/10.1117/12.2283079
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Machine vision plays an important part in industrial online inspection. Owing to the nonuniform illuminance conditions and variable working distances, the captured image tends to be over-exposed or under-exposed. As a result, when processing the image such as crack inspection, the algorithm complexity and computing time increase. Multiexposure high dynamic range (HDR) image synthesis is used to improve the quality of the captured image, whose dynamic range is limited. Inevitably, camera shake will result in ghost effect, which blurs the synthesis image to some extent. However, existed exposure fusion algorithms assume that the input images are either perfectly aligned or captured in the same scene. These assumptions limit the application. At present, widely used registration based on Scale Invariant Feature Transform (SIFT) is usually time consuming. In order to rapidly obtain a high quality HDR image without ghost effect, we come up with an efficient Low Dynamic Range (LDR) images capturing approach and propose a registration method based on ORiented Brief (ORB) and histogram equalization which can eliminate the illumination differences between the LDR images. The fusion is performed after alignment. The experiment results demonstrate that the proposed method is robust to illumination changes and local geometric distortion. Comparing with other exposure fusion methods, our method is more efficient and can produce HDR images without ghost effect by registering and fusing four multi-exposure images.
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Xudong Li, Xudong Li, Yongfu Chen, Yongfu Chen, Hongzhi Jiang, Hongzhi Jiang, Huijie Zhao, Huijie Zhao, } "Multi-exposure high dynamic range image synthesis with camera shake correction", Proc. SPIE 10458, AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing, 104580I (24 October 2017); doi: 10.1117/12.2283079; https://doi.org/10.1117/12.2283079

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