add other deep cgh paper comments

Author: bchao1

README.md

@@ -66,9 +66,9 @@ Recent advances in deep learning for CGH leverages camera-in-the-loop (CITL) tra
 - [Learned Hardware-in-the-loop Phase Retrieval for Holographic Near-Eye Displays](https://light.princeton.edu/publication/hil-holography/) (Chakravarthula et al. 2020) uses CITL to learn an aberration approximator that models the residual between holograms generated from ideal wave propagation (i.e. ASM) and real-world wave propagation models. An adversarial loss is used in addition to reconstruction loss to optimize the synthesized holograms.
 
 Previous works assume a naive wave propagation model (i.e. the angular spectrum method), and directly regresses complex holograms using different CNN architectures:
-- [Towards real-time photorealistic 3D holography with deep neural networks](https://cdfg.mit.edu/publications/tensor-holography) (Shi et al. 2021)
-- [DeepCGH: 3D computer-generated holography using deep learning](https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-18-26636&id=437573) (Eybposh et al. 2020)
-- [Deep neural network for multi-depth hologram generation and its training strategy](https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-18-27137&id=437709) (Lee et al. 2020)
+- [Towards real-time photorealistic 3D holography with deep neural networks](https://cdfg.mit.edu/publications/tensor-holography) (Shi et al. 2021) 
+- [DeepCGH: 3D computer-generated holography using deep learning](https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-18-26636&id=437573) (Eybposh et al. 2020) uses a CNN to estimate a complex field at a fixed plane from a set of 3D target multiplane inputs; the complex field is then reverse propagated to the SLM plane to generate a phase pattern.
+- [Deep neural network for multi-depth hologram generation and its training strategy](https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-18-27137&id=437709) (Lee et al. 2020) directly estimates the SLM phase pattern from 3D target multiplane inputs using a CNN.
 - [Deep-learning-generated holography](https://opg.optica.org/ao/abstract.cfm?uri=ao-57-14-3859) (Horisaki et al. 2018)
 - [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://www.nature.com/articles/lsa2017141) (Rivenson et al. 2018)