Add to BibSonomy
Abstract
We present an unsupervised learning denoising method, RepE (representation and enhancement), designed for nonlinear optical microscopy images, such as second harmonic generation (SHG) and two-photon fluorescence (TPEF). Addressing the challenge of effectively denoising images with various noise types, RepE employs an encoder network to learn noise-free representations and a reconstruction network to generate denoised images. It offers several key advantages, including its ability to (i) operate without restrictive statistic assumptions, (ii) eliminate the need for clean-noisy pairs, and (iii) requires only a few training images. Comparative evaluations on real-world SHG and TPEF images from esophageal cancer tissue slides (ESCC) demonstrate that our method outperforms existing techniques in image quality metrics. The proposed method provides a practical, robust solution for denoising nonlinear optical microscopy images, and it has the potential to be extended to other nonlinear optical microscopy modalities.
© 2023 Optica Publishing Group
Full Article | PDF ArticleMore Like This
Lejia Hu, Shuwen Hu, Wei Gong, and Ke Si
Opt. Lett. 46(9) 2055-2058 (2021)
Matthew Thomas, Santosh Kumar, and Yu-Ping Huang
Opt. Lett. 48(16) 4320-4323 (2023)
Pedram Abdolghader, Andrew Ridsdale, Tassos Grammatikopoulos, Gavin Resch, François Légaré, Albert Stolow, Adrian F. Pegoraro, and Isaac Tamblyn
Opt. Express 29(21) 34205-34219 (2021)