UAV image stitching method based on diffusion model and manifold gradient constraint

doi:10.7523/j.ucas.2024.061

Abstract

Abstract: Image stitching is a crucial prerequisite step for unmanned aerial vehicle (UAV) remote sensing applications, while the stitched images using most of current image stitching methods often suffer large irregular boundaries and multiple stitching seams, which can seriously affect subsequent analysis and applications. Existing improved methods typically cannot simultaneously address these two issues, and integrating the two types of methods in sequence is a straightforward way to solve the two problems, while this cannot often obtain satisfactory performance because of the inevitable error propagation problem. This paper proposes an inpainting method for the UAV image stitching task based on the Denoising Diffusion Probability Model (DDPM). The method uniformly designs masks for irregular boundaries and stitching seams, and a diffusion model is then utilized with manifold gradient prior constraints to complete the masked regions. By doAAAing so, both irregular boundaries and stitching seams are simultaneously eliminated, thereby improving the quality of the stitching results. Comparative experiments are conducted using four datasets established for different scenarios. Comparative experiments leveraging data from diverse scenarios were conducted. The experimental results demonstrated the efficacy of the proposed method in effectively eliminating irregular boundaries and seams in the image stitching. Moreover, the patches quality to pictures quality (PaQ-2-PiQ) and multi-scale image quality (MUSIQ) scores were improved 4.36% and 15.37%, respectively. Furthermore, at the locations of irregular boundaries, the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) values improved 20.22% and 33.69%, respectively, with the proposed restoration. Compared with state-of-the-art methods and other conventional image stitching algorithms, the proposed method performs better than other methods in both subjective and objective quality metric scores, has good robustness and generalization, and can be widely applied to UAV image stitching scenarios.

Key words: UAV image, diffusion model, image stitching, irregular boundaries, stitching seams

CLC Number:

TP751.1

WANG Jie, LUO Yongxi, CHEN Jun, WU Yewei. UAV image stitching method based on diffusion model and manifold gradient constraint[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.061.

References

[1] He K M, Chang H W, Sun J.Rectangling panoramic images via warping[J]. ACM Transactions on Graphics, 2013, 32(4):79. DOI: 10.1145/2461912.2462004.
[2] Li D P, He K M, Sun J, et al.A geodesic-preserving method for image warping[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA. IEEE, 2015:213-221.DOI: 10.1109/CVPR.2015.7298617.
[3] Zhang Y, Lai Y K, Zhang F L.Content-preserving image stitching with piecewise rectangular boundary constraints[J]. IEEE Transactions on Visualization and Computer Graphics, 2021, 27(7): 3198-3212. DOI: 10.1109/TVCG.2020.2965097.
[4] Nie L, Lin C Y, Liao K, et al.Deep rectangling for image stitching: A learning baseline[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA. IEEE, 2022: 5730-5738. DOI: 10.1109/CVPR52688.2022.00565.
[5] Kwatra V, Schödl A, Essa I, et al.Graphcut textures: Image and video synthesis using graph cuts[J]. ACM Transactions on Graphics, 2003, 22(3): 277-286. DOI: 10.1145/882262.882264.
[6] Eden A, Uyttendaele M, Szeliski R.Seamless image stitching of scenes with large motions and exposure differences[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). New York, NY, USA. IEEE, 2006: 2498-2505. DOI: 10.1109/CVPR.2006.268.
[7] Li N, Liao T L, Wang C.Perception-based seam cutting for image stitching[J]. Signal, Image and Video Processing, 2018, 12(5): 967-974. DOI: 10.1007/s11760-018-1241-9.
[8] Gao J, Li Y, Chin T J,et al.Seam-Driven Image Stitching[C]//Eurographics 2013.2013.DOI:10.2312/CONF/EG2013/SHORT/045-048.
[9] Zhang F, Liu F.Parallax-tolerant image stitching[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA. IEEE, 2014: 3262-3269. DOI: 10.1109/CVPR.2014.423.
[10] Lin K M, Jiang N J, Cheong L F, et al. SEAGULL: seam-guided local alignment for parallax-tolerant image stitching[C]//European Conference on Computer Vision. Cham: Springer, 2016: 370-385.10.1007/978-3-319-46487-9_23.
[11] Dai Q Y, Fang F M, Li J C, et al.Edge-guided composition network for image stitching[J]. Pattern Recognition, 2021, 118: 108019. DOI: 10.1016/j.patcog.2021.108019.
[12] Nie L, Lin C Y, Liao K, et al.Parallax-tolerant unsupervised deep image stitching[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV). Paris, France. IEEE, 2023: 7365-7374. DOI: 10.1109/ICCV51070.2023.00680.
[13] Sohl-Dickstein J, Weiss E A, Maheswaranathan N, et al.Deep unsupervised learning using nonequilibrium thermodynamics[C]//Proceedings of the 32^nd International Conference on International Conference on Machine Learning - Volume 37. July 6 - 11, 2015, Lille, France. ACM, 2015: 2256-2265. DOI: 10.5555/3045118.3045358.
[14] Ho J, Jain A, Abbeel P.Denoising diffusion probabilistic models[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. December 6 - 12, 2020,Vancouver, BC, Canada. ACM, 2020: 6840-6851. DOI: 10.5555/3495724.3496298.
[15] Nichol A, Dhariwal P. Improved denoising diffusion probabilistic models[EB/OL]. arXiv:2102.09672. (2021-02-18)[2024-05-20]. DOI: 10.48550/arXiv.2102.09672.
[16] Zhu Y Z, Zhang K, Liang J Y, et al.Denoising diffusion models for plug-and-play image restoration[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Vancouver, BC, Canada. IEEE, 2023: 1219-1229. DOI: 10.1109/CVPRW59228.2023.00129.
[17] Lugmayr A, Danelljan M, Romero A, et al.RePaint: Inpainting using denoising diffusion probabilistic models[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA. IEEE, 2022: 11451-11461. DOI: 10.1109/CVPR52688.2022.01117.
[18] Chung H, Sim B, Ryu D, et al.Improving diffusion models for inverse problems using manifold constraints[C]//Proceedings of the 36th International Conference on Neural Information Processing Systems. 28 November 2022, New Orleans, LA, USA. ACM, 2022: 25683-25696. DOI: 10.5555/3600270.3602132.
[19] Dhariwal P, Nichol A. Diffusion models beat GANs on image synthesis[EB/OL]. 2021: 2105.05233.http://arxiv.org/abs/2105.05233v4.
[20] Lowe D G.Object recognition from local scale-invariant features[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision. Kerkyra, Greece. IEEE, 1999: 1150-1157. DOI: 10.1109/ICCV.1999.790410.
[21] Fischler M A, Bolles R C.Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography[M]//Readings in Computer Vision. Amsterdam: Elsevier, 1987: 726-740. DOI: 10.1016/b978-0-08-051581-6.50070-2.
[22] Chen Y B, Liu S F, Wang X L.Learning continuous image representation with local implicit image function[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA. IEEE, 2021: 8624-8634. DOI: 10.1109/CVPR46437.2021.00852.
[23] Lim B, Son S, Kim H, et al.Enhanced deep residual networks for single image super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, HI, USA. IEEE, 2017: 1132-1140. DOI: 10.1109/CVPRW.2017.151.
[24] Ke J J, Wang Q F, Wang Y L, et al.MUSIQ: Multi-scale image quality transformer[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada. IEEE, 2021: 5128-5137. DOI: 10.1109/ICCV48922.2021.00510.
[25] Ying Z Q, Niu H R, Gupta P, et al.From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, WA, USA. IEEE, 2020: 3572-3582. DOI: 10.1109/CVPR42600.2020.00363.
[26] Jia Q, Li Z J, Fan X, et al.Leveraging line-point consistence to preserve structures for wide parallax image stitching[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA. IEEE, 2021: 12181-12190. DOI: 10.1109/CVPR46437.2021.01201.
[27] Liao T L, Li N.Single-perspective warps in natural image stitching[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2019. DOI: 10.1109/TIP.2019.2934344.
[28] Lin C C, Pankanti S U, Ramamurthy K N, et al.Adaptive as-natural-as-possible image stitching[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA. IEEE, 2015: 1155-1163. DOI: 10.1109/CVPR.2015.7298719.
[29] Zaragoza J, Chin T J, Brown M S, et al.As-projective-As-possible image stitching with moving DLT[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR, USA. IEEE, 2013: 2339-2346. DOI: 10.1109/CVPR.2013.303.