@article{Agrafiotis2018,

title = {Underwater photogrammetry in very shallow waters: Main challenges and caustics effect removal},

author = {P Agrafiotis and D Skarlatos and T Forbes and C Poullis and M Skamantzari and A Georgopoulos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/15/2018/},

doi = {10.5194/isprs-archives-XLII-2-15-2018},

issn = {16821750},

year  = {2018},

date = {2018-05-01},

journal = {Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch.},

volume = {42},

number = {2},

pages = {15--22},

publisher = {International Society for Photogrammetry and Remote Sensing},

abstract = {In this paper, main challenges of underwater photogrammetry in shallow waters are described and analysed. The very short camera to object distance in such cases, as well as buoyancy issues, wave effects and turbidity of the waters are challenges to be resolved. Additionally, the major challenge of all, caustics, is addressed by a new approach for caustics removal (Forbes et al., 2018) which is applied in order to investigate its performance in terms of SfM-MVS and 3D reconstruction results. In the proposed approach the complex problem of removing caustics effects is addressed by classifying and then removing them from the images. We propose and test a novel solution based on two small and easily trainable Convolutional Neural Networks (CNNs). Real ground truth for caustics is not easily available. We show how a small set of synthetic data can be used to train the network and later transfer the le arning to real data with robustness to intra-class variation. The proposed solution results in caustic-free images which can be further used for other tasks as may be needed.},

keywords = {Caustics, CNN, SfM MVS, Underwater 3D reconstruction},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2017a,

title = {The effect of underwater imagery radiometry on 3D reconstruction and orthoimagery},

author = {P Agrafiotis and G I Drakonakis and A Georgopoulos and D Skarlatos},

url = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W3/25/2017/},

doi = {10.5194/isprs-archives-XLII-2-W3-25-2017},

issn = {16821750},

year  = {2017},

date = {2017-02-01},

journal = {Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch.},

volume = {42},

number = {2W3},

pages = {25--31},

abstract = {The work presented in this paper investigates the effect of the radiometry of the underwater imagery on automating the 3D reconstruction and the produced orthoimagery. Main aim is to investigate whether pre-processing of the underwater imagery improves the 3D reconstruction using automated SfM - MVS software or not. Since the processing of images either separately or in batch is a time-consuming procedure, it is critical to determine the necessity of implementing colour correction and enhancement before the SfM - MVS procedure or directly to the final orthoimage when the orthoimagery is the deliverable. Two different test sites were used to capture imagery ensuring different environmental conditions, depth and complexity. Three different image correction methods are applied: A very simple automated method using Adobe Photoshop, a developed colour correction algorithm using the CLAHE (Zuiderveld, 1994) method and an implementation of the algorithm described in Bianco et al., (2015). The produced point clouds using the initial and the corrected imagery are then being compared and evaluated.},

keywords = {SfM-MVS, Underwater 3D reconstruction, Underwater image enhancement},

pubstate = {published},

tppubtype = {article}

}