fixing section link

README.md

@@ -4,7 +4,7 @@
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10809893.svg)](https://doi.org/10.5281/zenodo.10809893)
 [![License](https://img.shields.io/badge/License-BSD_3--Clause-purple.svg)](https://opensource.org/licenses/BSD-3-Clause)
 
-Bayesian multi-exposure image fusion (MEF) is a general-purpose algorithm to achieve robust high dynamic range (HDR) imaging, particularly in scenarios with low signal-to-noise ratio (SNR) or variations in illumination intensity. This approach is especially crucial for high quality phase retrieval in coherent diffractive imaging (CDI). The algorithm, detailed in the [arXiv preprint](https://arxiv.org/abs/2403.11344), primarily focuses on its implementation and demonstrates the benefits in ptychography. To reproduce the results in the paper, see this [section](https://github.com/microscopic-image-analysis/bayes-mef/edit/main/README.md#reproducing-results). However, to get started, please install the package and check the demo usage below.
+Bayesian multi-exposure image fusion (MEF) is a general-purpose algorithm to achieve robust high dynamic range (HDR) imaging, particularly in scenarios with low signal-to-noise ratio (SNR) or variations in illumination intensity. This approach is especially crucial for high quality phase retrieval in coherent diffractive imaging (CDI). The algorithm, detailed in the [arXiv preprint](https://arxiv.org/abs/2403.11344), primarily focuses on its implementation and demonstrates the benefits in ptychography. To reproduce the results in the paper, see this [section](#reproducing-results). However, to get started, please install the package and check the demo usage below.
 
 ![demo_mef](https://github.com/microscopic-image-analysis/bayes-mef/assets/64919085/d00a8c5e-5e53-4b7e-856b-381cc99523ba)