Depth Map Reconstruction for Scleral Bleb Models Using Structured Light in Light Field Imaging

Light field image processing offers promising opportunities for the development of three-dimensional object imaging technology in healthcare. However, implementing it in real-world scenarios often encounters numerous external challenges that can degrade the quality of captured data. One example is imaging blebs on the eye sclera after trabeculectomy. The surface of the blebs has a glossy appearance and, in some cases, contains minimal blood vessels, resulting in a predominantly white appearance. This condition leads to inaccuracies in depth map reconstruction from light field images. A depth map reconstruction method is introduced for bleb surface models on the eye sclera model, utilizing information from the central of the sub-aperture image as a boundary condition within a Markov Random Field. This approach reduces outliers at the edges of the depth map where intensity variations occur. Additionally, the use of structured light on textureless object surfaces serves as an auxiliary enhancement, adding contour detail to the clear and white bleb surface model. Four Heidelberg Collaboratory for Image Processing datasets were used to evaluate the performance of this approach, with the number of sub-aperture images was reduced to match the amount of data generated by the light field camera employed in this study. Based on the tests conducted, the proposed method improves the accuracy of depth map reconstruction for the input dataset. Furthermore, the addition of structured light on textureless bleb model images significantly reduces the depth map reconstruction errors while enhancing the similarity index of the reconstructed depth maps relative to the ground truth.