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paper/agujournaltemplate.aux

@@ -10,7 +10,6 @@
 \newlabel{tab: example}{{1}{5}{}{}{}}
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+\@writefile{toc}{\contentsline {section}{\numberline {7}Conclusions and future work}{5}{}\protected@file@percent }
+\@writefile{toc}{\contentsline {section}{\numberline {8}Open Research}{6}{}\protected@file@percent }
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paper/agujournaltemplate.fdb_latexmk

@@ -1,5 +1,5 @@
 # Fdb version 4
-["pdflatex"] 1735910625.20657 "agujournaltemplate.tex" "agujournaltemplate.pdf" "agujournaltemplate" 1735910625.5816 2
+["pdflatex"] 1736183374.60667 "agujournaltemplate.tex" "agujournaltemplate.pdf" "agujournaltemplate" 1736183374.99093 2
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@@ -91,8 +91,8 @@
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-  "agujournaltemplate.tex" 1735910625.12692 14141 57b4f6bb05855c3c3c8a398b892b4bdc ""
+  "agujournaltemplate.aux" 1736183374.92334 1223 272e134bb5e62a2e9e5d7df31a2ea00b "pdflatex"
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   "trackchanges.sty" 1731690459.80047 26287 09689e5e9db973d136f0537b8570fadf ""
   (generated)
   "agujournaltemplate.aux"

paper/agujournaltemplate.tex

@@ -148,8 +148,6 @@ \section{Data}
 \section{Training}
 This model, as seen in Table \ref{tab: example}, was pre-trained using Adam optimizer, with a learning rate of 0.0002 (lr) and gradient clip at 0.005 (gc) for 20 hours with a Nvidia RTX 4070 dropping the physical constraints from the loss function. Then fine-tuned with a learning rate of 0.00005 and gradient clip of 0.0005 including the physical constraint terms.
 
-\section{Results}
-
 
 \section{Conclusions and future work}
 Our work proposes a novel framework for image calibration and image restoration, effectively improving fuzzy logic mechanism for missing blocks inpainting. The architecture used in this work could have been more robust adding more layers, but the computational resources available restraint this possibility, that's why it's recommended to try this architectures with more layers. Also, including local residual connections for the encoder and decoder separately could enhance information flow and furthermore the performance in general. Ultimately, another suitable approach could be a multi-modal network that analyses the position, and time between different images, generating a joint calibration routine that would improve CME dynamics capture. The GitHub repository where the source code is allocated is accessible to the reader in the following link: \url{https://github.com/Jorgedavyd/DL-based-Coronagraph-Inpainting}