Abstract
3-D imaging techniques suffer from noise and deterioration of image quality. This work explores an unsupervised deep learning method for integral imaging denoising using a single shot that overcomes the problem of limited clean data.
© 2023 The Author(s)
PDF Article | Presentation VideoMore Like This
Jonas Nienhaus, Anja Britten, Philipp Matten, Thomas Schlegl, Katharina Dettelbacher, Andreas Pollreisz, Wolfgang Drexler, Rainer A. Leitgeb, and Tilman Schmoll
126321Q European Conference on Biomedical Optics (ECBO) 2023
Vineela Chandra Dodda, Lakshmi Kuruguntla, Karthikeyan Elumalai, Inbarasan Muniraj, and Sunil Chinnadurai
JW2A.19 3D Image Acquisition and Display: Technology, Perception and Applications (3D) 2022
Wei-Ju Chen, En-Yu Liao, Tsung-Ming Tai, Yi-Hua Liao, Chi-Kuang Sun, Cheng-Kuang Lee, Simon See, and Hung-Wen Chen
ATu3Q.6 CLEO: Applications and Technology (CLEO:A&T) 2023