A two-stage restoration of distorted underwater images using compressive sensing and image registration
Zhen Zhang, Yu-Gui Tang, Kuo Yang
Advances in Manufacturing ›› 2021, Vol. 9 ›› Issue (2) : 273-285.
A two-stage restoration of distorted underwater images using compressive sensing and image registration
Imaging through a time-varying water surface exhibits severe non-rigid geometric distortions and motion blur. Theoretically, although the water surface possesses smoothness and temporal periodicity, random fluctuations are inevitable in an actual video sequence. Meanwhile, considering the distribution of information, the image structure contributes more to the restoration. In this paper, a new two-stage restoration method for distorted underwater video sequences is presented. During the first stage, salient feature points, which are selected through multiple methods, are tracked across the frames, and the motion fields at all pixels are estimated using a compressive sensing solver to remove the periodic distortions. During the second stage, the combination of a guided filter algorithm and an image registration method is applied to remove the structural-information-oriented residual distortions. Finally, the experiment results show that the method outperforms other state-of-the-art approaches in terms of the recovery effect and time.
Periodic wave / Random wave / Compressive sensing (CS) / Image registration (IR) / Structural information
[1.] |
|
[2.] |
|
[3.] |
|
[4.] |
|
[5.] |
|
[6.] |
|
[7.] |
|
[8.] |
|
[9.] |
Tian Y, Narasimhan SG (2009) Seeing through water: image restoration using model-based tracking. In: Proceedings of the IEEE 12th international conference on computer vision. IEEE, Kyoto, pp 2303–2310
|
[10.] |
|
[11.] |
Efros A, Isler V, Shi J et al (2004) Seeing through water. In: Proceedings of conference and workshop on neural information processing systems. Neural Information Processing Systems Foundation, Vancouver, pp 393–400
|
[12.] |
Donate A, Dahme G, Ribeiro E (2006) Classification of textures distorted by water waves. In: Proceedings of international conference on pattern recognition. IEEE, Hong Kong, pp 421–424
|
[13.] |
Donate A, Ribeiro E (2006) Improved reconstruction of images distorted by water waves. In: Advances in computer graphics and computer vision. Springer, Berlin, pp 264–277
|
[14.] |
|
[15.] |
Kanaev AV, Ackerman J, Fleet E et al (2009) Imaging through the air-water interface. In: Proceedings of computational optical sensing and imaging. OSA, San Jose, pp 13–15
|
[16.] |
|
[17.] |
Kanaev AV, Hou W, Restaino SR et al (2014) Correction methods for underwater turbulence degraded imaging. In: Proceedings of remote sensing of clouds and the atmosphere XIX; and optics in atmosphereic propagation and adaptive systems XVII. Proc. SPIE, 92421P
|
[18.] |
|
[19.] |
|
[20.] |
|
[21.] |
Oreifej O, Guang S, Pace T et al (2011) A two-stage reconstruction approach for seeing through water. In: Proceedings of IEEE conference on computer vision and pattern recognition. IEEE, Colorado, pp 1153–1160
|
[22.] |
|
[23.] |
|
[24.] |
Li Z, Murez Z, Kriegman D et al (2018) Learning to see through turbulent water. In: Proceedings of 2018 IEEE winter conference on applications of computer vision. IEEE, Lake Tahoe, pp 512–520
|
[25.] |
|
[26.] |
James JG, Agrawal P, Rajwade A (2019) Restoration of non-rigidly distorted underwater image using a combination of compressive sensing and local polynomial image representations. In: Proceedings of the IEEE international conference on computer vision. IEEE, Seoul, pp 7839–7848
|
[27.] |
|
[28.] |
Bay H, Tuytelaars T, Van GL (2006) Surf: speeded up robust features. In: Proceedings of European conference on computer vision. Graz, pp 404–417
|
[29.] |
|
[30.] |
Leutenegger S, Chli M, Siegwart RY (2011) BRISK: Binary robust invariant scalable keypoints. In: Proceedings of international conference on computer vision. IEEE, Barcelona, pp 2548–2555
|
[31.] |
|
[32.] |
|
[33.] |
|
[34.] |
|
[35.] |
|
[36.] |
|
[37.] |
|
/
〈 |
|
〉 |