Research on underwater image registration method based on MSRCR and SIFT

Wenrui Zhang; Jianming Miao; Minfu Li; Huanrong Liu

doi:10.1117/12.2626893

2 March 2022 Research on underwater image registration method based on MSRCR and SIFT

Wenrui Zhang, Jianming Miao, Minfu Li, Huanrong Liu

Proceedings Volume 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021); 121580D (2022) https://doi.org/10.1117/12.2626893
Event: 2021 International Conference on Computer Vision and Pattern Analysis, 2021, Guangzhou, China

Abstract

Due to the complex and varying underwater environment, the optical images obtained are low resolution and full of noise, which ultimately make it difficult to register and stich the optical images. To solve the above problems, we propose an improved image registration method based on MSRCR and SIFT. First, multi-scale retinex with color restoration (MSRCR) is applied to improve the underwater low-quality image, and the image contrast is improved by contrast limited adaptive histogram equalization (CLAHE). After that, the scale-invariant feature transform (SIFT) algorithm is adopted to extract feature points of the reference image and sensed image. Then coarsely match the feature points based on the K nearest neighbor (KNN)-based Ratio Matching algorithm, and the random sample consensus (RANSAC) is used to eliminate mismatched feature points and improve matching accuracy. Finally, the mosaic image is output after the transformation matrix is calculated. The experiment results show a better effect of underwater image registration and stitching, demonstrating that the algorithm can improve the accuracy of underwater image registration through image enhancement.

Citation Download Citation

Wenrui Zhang, Jianming Miao, Minfu Li, and Huanrong Liu "Research on underwater image registration method based on MSRCR and SIFT", Proc. SPIE 12158, International Conference on Computer Vision and Pattern Analysis (ICCPA 2021), 121580D (2 March 2022); https://doi.org/10.1117/12.2626893

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