Image Mosaic Based on Local Guidance and Dark Channel Prior

Image mosaic is of significance in various areas such as object tracking and drone reconnaissance. Aiming at the problems of poor performance and high rate of false match in foggy images, an improved stitching method based on local guided KAZE and dark channel prior is proposed. First of all, the KAZE algorithm is utilized for rough feature matching. Secondly, a local fixed point asymptotic method is introduced to optimize the global objective and eliminate mismatched point pairs. Then, the warp images are obtained by the estimated transformation matrix. Thirdly, the compensation of color and luminance difference of the overlap is applied to the overall image, which improves the inhomogeneity of stitching image. Eventually, the final result is obtained by enhancing algorithm based on dark channel prior. The proposed algorithm is assessed through intuitive renderings and quantitative values. Furthermore, the proposed method is compared with other common stitching methods. The results reveal that the method proposed in this paper gives the best performance in terms of the magnitude of feature matching pairs, the mean absolute error (MAE), the root mean square error (RMSE) and the processing time.