Deep‐sea image stitching: Using multi‐channel fusion and improved AKAZE

Abstract

AbstractDeep‐sea image is of great significance for exploring seabed resources. However, the information of a single image is limited. Besides, deep‐sea image with low contrast and colour distortion further restricts useful feature extraction. To address the issues above, this paper presents a multi‐channel fusion and accelerated‐KAZE (AKAZE) feature detection algorithm for deep‐sea image stitching. First, the authors restore deep‐sea image in LAB colour space and RGB colour space, respectively; in LAB space, the authors use homomorphic filtering in L colour channel, and in RGB space, the authors adopt multi‐scale Retinex with chromaticity preservation algorithm to adjust the colour information. Then, the authors blend two pre‐processed images with dark channel prior weighted coefficient. After that, the authors detect feature points with the AKAZE algorithm and obtain feature descriptors with Boosted Efficient Binary Local Image Descriptor. Finally, the authors match the feature points and warp deep‐sea images to obtain the stitched image. Experimental results demonstrate that the authors’ method generates high‐quality stitched image with minimized seam. Compared with state‐of‐the‐art algorithms, the proposed method has better quantitative evaluation, visual stitching results, and robustness.